{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>099</td>\n",
       "      <td>090500</td>\n",
       "      <td>13099090500</td>\n",
       "      <td>905</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>340970567</td>\n",
       "      <td>3670838</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-85.11460 31.27733, -85.11449 31.277...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>099</td>\n",
       "      <td>090400</td>\n",
       "      <td>13099090400</td>\n",
       "      <td>904</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>202255980</td>\n",
       "      <td>924285</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-84.99042 31.27632, -84.99037 31.277...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>099</td>\n",
       "      <td>090200</td>\n",
       "      <td>13099090200</td>\n",
       "      <td>902</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>297097657</td>\n",
       "      <td>2960992</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-85.08718 31.37407, -85.08691 31.374...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>067</td>\n",
       "      <td>031209</td>\n",
       "      <td>13067031209</td>\n",
       "      <td>312.09</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>3407093</td>\n",
       "      <td>4863</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-84.51004 33.85250, -84.50997 33.852...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>067</td>\n",
       "      <td>031207</td>\n",
       "      <td>13067031207</td>\n",
       "      <td>312.07</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2775451</td>\n",
       "      <td>49508</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-84.47968 33.86431, -84.47805 33.868...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20  NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        13        099    090500  13099090500     905  Census Tract   G5020   \n",
       "1        13        099    090400  13099090400     904  Census Tract   G5020   \n",
       "2        13        099    090200  13099090200     902  Census Tract   G5020   \n",
       "3        13        067    031209  13067031209  312.09  Census Tract   G5020   \n",
       "4        13        067    031207  13067031207  312.07  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  340970567   3670838  ...        0        0        0        0   \n",
       "1          S  202255980    924285  ...       42       42        0        0   \n",
       "2          S  297097657   2960992  ...        0        0        0        0   \n",
       "3          S    3407093      4863  ...        0        0        0        0   \n",
       "4          S    2775451     49508  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0        0        0        0        0   \n",
       "2        0        0        0        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-85.11460 31.27733, -85.11449 31.277...  \n",
       "1  POLYGON ((-84.99042 31.27632, -84.99037 31.277...  \n",
       "2  POLYGON ((-85.08718 31.37407, -85.08691 31.374...  \n",
       "3  POLYGON ((-84.51004 33.85250, -84.50997 33.852...  \n",
       "4  POLYGON ((-84.47968 33.86431, -84.47805 33.868...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/ga_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 2796 popn tracts for GA\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"GA\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population GA voter-age population\n",
      "state pop= 10711908 VAP pct Hispanic is  0.09037630619125347\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP GA\n",
      "total state pop= 10711908 , VAP pct Black is  0.3027172816867175\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for GA\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for GA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "166 0 0.00026515533650001675 nonPolygon tract no, pop, area\n",
      "0.0002498582830000143\n",
      "1.529705350000246e-05\n",
      "261 1509 0.03382349554150026 nonPolygon tract no, pop, area\n",
      "0.00042630844900011793\n",
      "0.03339718709250015\n",
      "929 2768 0.006130828092499979 nonPolygon tract no, pop, area\n",
      "1.818060699999046e-05\n",
      "0.006112647485499988\n",
      "1267 2200 0.014319799386499844 nonPolygon tract no, pop, area\n",
      "0.0006330850884999921\n",
      "0.013686714297999851\n",
      "1665 4811 0.019632654954000003 nonPolygon tract no, pop, area\n",
      "0.019560502891999954\n",
      "7.215206200004946e-05\n",
      "2459 1900 0.0379090063455 nonPolygon tract no, pop, area\n",
      "7.956522999996036e-05\n",
      "0.03782944111550004\n",
      "2751 2551 0.040442952405000135 nonPolygon tract no, pop, area\n",
      "0.000138082617999976\n",
      "0.04030486978700016\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for GA)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[54, 166, 557, 558, 578, 856, 919, 1056, 1168, 1193, 1336, 1343]\n"
     ]
    },
    {
     "data": {
      "image/png": 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dMycfPsnChQtZunQp7dq1o3LlysycORMwfum6f/9+tNZERUXRpUsXAD799FMGDBhATk4O0dHRNGvW7NYa+jYorXXxa5WziIgIHRsbW/yKQgiborUm9n+xJHybwInfTqCUQudrIoZF0OXzLiiHwrttci7lcPGvi1z46wIXTl/g4pmLZKVkkZ+Xj3JQ/LjuR1IyU3hp4Et4VvPk/MnzvPz2y9xz7h5qUpNG/Rox6eQkWrdpzebNmwkNDWXixIm4uLgQExPD119/ja+vL1FRUSxYsIDAwMBCazm65iiHlh/i8IrD/LX7LxoPaEyPb3rg4Gi741eUUnFa64iClskZvRCi1CiluHfYvdw77F4ALpy+wIoRK4j9byzxc+Np+kxT7n3+Xnzu9rlpW2c3Z7xqeeFVy6vA9z5S7QjJycnc+/y95nl1t9alY8OOHPzXQeLnxnPI5xC9Hu3F2rVree2115g+fTpDhw5lwIABNG3aFIPBQO/evYsMeYDa0bWpHV2bB8Y9wJULV3D2cLb57xaKYrsfT0KICq/SXZXoOacnnSZ0wt3XnS0TtzCpziS2/Xdbqby/UooabWrwzO/PGGekQqVdlcjPyycmJobdu3dz/vx5xowZw/79+zl8+DCJiYn88ccfFu/DpVLpfoF8bP0xlv9jObtm7iLzZGapvW9Rij2jV0oZgA2Aq2n9+Vrr0Uqp8UB34ApwGBiktU4vYPsY4FPAEZiqtR5beuULIaztxRdfJDY2lry8PEaMGEFgYCCPP/449evXB2DChAm0GtGKFi+34P4q93Pi4gm+fv5rXvjhBf659p+lUkNwy2DajmzL6g9Ws3TqUo5PPc6loZeoU7cODg4OuLi4UKlSJRwdHfH29iYtLa1U9nsrtk/ZTvzcePO0f6g/D894mKDmQWW3U611kT+AAiqZXjsDW4GWQCfAyTT/P8B/CtjWEeOHQAjgAuwCGha3z+bNm2shhO05k3hGf9/re7110lattdbx8fE6KipKa611ZmamDgkJ0WvXrtXPPPPMTdtOnjxZf/nll/pS2iU9hjF6DGP0po82WbTfwYMH64YNG+q7775b9+jRQ2dkZOj7779fBwYG6oiICP32229rrbVOOpCkw9zDdE1q6nu4R3/a+FOttdafffaZvvfee3Xr1q31wIEDdW5ubmk0xy05veu0/qzOZ3oMY/TK11fqj2t8rD+o9IE+tPLQbb0vEKsLydQSfRmrlHIHfgOGaa23XjP/EeBRrXX/G9ZvBYzRWnc2Tb9p+nD5sKj9yJexQtiefT/uY37f+eRlG+/h0uTJJrT5TxsGDBzA0qVLSUlJoUePHowfP54nnniCWrVqER4ezrhx43BzcyM6OppWrVqxefNm7ql3DwHTAnDIc+CJ1U8Qcn9IqdWptWb1v1azedxmANr/X3s6/LtDqb1/adg6aSvLX1rO83uex+BlYHbMbM7uO0vPOT0JfezWbpVQ1JexFvXRK6UclVI7gTPAqmtD3uRp4JcCNq0GJF0znWyaV9A+hiilYpVSsSkpKZaUJYQoB1prds7YyfePfE9AaADDDw2n1aut2DVzF6uHrMaQYqBm1Zo0DmvMm2+8SfPmzTl48CAbN26kcuXK5qGOJ0+eJDAwkLVr1+Lh6UH2o9kAzOo4i6JOOLVpnH7u5VyL6lVK8cB/HqDJk8aLlJJ/T77NFihd+37cx/KXlmPwNuB3jx+eQZ4M2jAIgN8/+r1M9mnRqButdR4QrpTyAhYppcK01gkASqlRQC4wp4BNC/oGo8B/Ua31FGAKGM/oLalLCFG2sjOzmd15Nslbkgm6N4inf3saRxdHHhj/AHlX8vhu8nccyDvAszzLZS7zQv8XOPjXQQyeBgD69+/Pm2++CRivKI2JiQEgJiaGhQsX4oYbAOP9xhP+dDiBzQI5k3CGlIQU9i/Zj1dtL9KPpgNQpWYVXj76skVfjG6dtJVdM3cB8OCkB0u7WW5Zyt4UNrxrvDdOr7m9rhtu6ujsSGBE0aOBblWJhldqrdOVUuuAGCBBKTUQ6Abcrwv+SE4Gql8zHQycusVahRDl6K/4v/hf4/8BEDkmklYjWuHoYry1gVKKBz97EGLAYZ4Dr7z9CnFfxzH13ansWbKH+568D4A1a9aYv5SNiooiNjaWOnXqmH/329mPbV9sI/1oeoFns1dDHiDjeAZLhy6lYa+G1GhXg/ycfNKPp+Pm7cbehXs5ufWk+ZYLp3ee5q6md9FvWT88Az3LuKWKprUmaVMSm8Zt4sBPB3Byc6LzJ52pE1MHgKyzWSwbtoycrByaDS6bi6mK7aNXSvkDOaaQdwNWYvzyNReYCERqrQvsa1FKOQEHgPuBk8A2oJ/Wek9R+5Q+eiGsb8unW1jxygoARuvRBa6Tn5/P008/zaFDh8j4K4PgQ8G0eaUNP278EXd3d/z8/Jg+fTpeXl6kpaUxaNAg0tPT8fHxYdasWXh4eJjf61TsKU78dgL/UH9cPV1x93PHp45xvH1+bj4LByxk36J95F3JQzkYL8S6VpUaVXDzdcO1siteNb3oNqUbTq7WvVTo4C8HCe0aSpAOwtHJkZ5detKwS0NmfDsDNJw4eAL3M+701r2p9mI1vtr6Fa6urnh4ePD999/j6Wn5h1RRffSWBH1jYAbGETQOwA9a638rpQ5hHHJ5zrTqFq31UKVUEMZhlF1M23cBPjFtP11r/X5xBUvQC2Ebvn/ke/b9uI9aUbUIeSCEkI4hBDYPLPAK0Z+G/MSumbt47fRrGLwMZVJPTlYOxzcc58SmE+ReyiX1YCp3NbuLsL5h+NX3K5N93qr4b+NZ2G8hnzt9zs+f/kz4U+E4uzuj8zV7ftjD+n+v55u93xAeEs77S97n+dHPM3z4cCIjIxkzZgxVq1Zl2LBhFu/vtq6M1VrvBpoWML9OIeufArpcM/0z8LPF1QohbEaniZ3wruPN0dVHWTNqDWtGrcHgZaBWh1rc3fluarSpgX9Df07vPM2O6Tto9myzMgt5AGd3Z+rE1DF3e9iq7MxsFvZbCECWcxavff8avqt9mTBhAttHbSfh2wR8Gvjwp/+frN+9HncPd0JDQ813xUxLS6NRo0alVo/c60YIYZGLZy5ydM1RDv58kITvEsw3LTN4GbicfhnloBhxcgSV7qpk5Uqt68CyA6wcsZLUw6nEfBpDSJ8Q/Pz8WLFiBSNfGMlDhx8icnQk6eHpLFq0iBkzZgCwe/duunfvjqenJ5UrV2bDhg04OVne9ST3uhFC3DaPAA/C+oYR1jeMh795mNTDqSRtTiJpUxJ/xv1J63+2vqNDPjszm+WvLGfn1zvxre/LgBUDrrs+oMq+Khw9fJTQPqFEjo6kT58+PPvss+blQ4cOZeHChTRv3pwPP/yQjz/+mH/+s3SuHJagF0KUmHJQ+Nb1xbeuL+EDw61djtUd33CcHwf+SMaJDNqNakfk25E4ujhy4cIF3Nzc+OOzP5g5Yia+3r48MvMRzp8/T1xcHN999535PbTW+Pv7AxAQEMChQ4dKrT4JeiGEuEW52bms/b+1bP5oM94h3gz6bRDVW/09onzb2m08/eTT5Kfn4+HnwQ/Lf8DRxZH5s+fz8MMP4+Dw95faY8eOpXfv3hgMBhwcHJg9e3ap1Sl99EIIcQv+2v0XCwcs5Ez8GZo/15xOH3XCpZKLeXleTh7TW0/nVOwpOrzbgTZvtDE/YrEsSB+9EEKUojN7zjD1vqm4eLrw+NLHqde13nXL/9z+Jyv+sYJTsad4ZPYjNO7f2EqVGknQCyFECWQkZTAz2vj4wCdWPcFdTe4CjM+bXdB3Aad3niYzORM3Hzd6fN3D6iEPEvRCCGGxvJw8FvRdQE5WDoP/GEzVRlXNy2L/F8uBpQeo0a4Gbd9sS6N+jcr0moKSkKAXQggLXEy5yNLnlpK0OYle3/Uyh7zO1/w68lc2f7SZet3r0XdxX5t77KAEvRBCFOFiykVW/GMFCd8mgILOH3cmrE+YefnRNUfZ9J9NNOrfiK7/7WpzIQ8S9EIIUSCtNfFz4ln+ynKyM7Np+Y+WhD8VTkBYgHmdzJOZrHx1JU4GJzpN6ISrp6sVKy6cBL0QQtxgQb8FxjN4jM+j7T61OwGhAWitObT8EKd3nSb1YCp7ftgDGvou7kulqrZ7VbAEvRBC3OBqyAMkb03my/AvcXByID8v33yPH3d/d+p0rkP0+9H41vO1VqkWkaAXQogb9Jzbk6VDlnLlwhUMVQwYvAy8duw1qjtVJ5982lRtw5zTxofqTZ8+nedCnyMnJ8fKVRfOomfGCiHEncRQxcCVC1cA8A7x5uWjL1Pz7prsPbeX532f56HWDwFw+fJlFi5cSPXq1Yt6O6uToBdCiBtcDfkWw1vw6A+PAnD69Gma3t2Ur899Ta2nagHw2WefMXTo0OvuWWOLbLs6IYSwgqqNjWPkgyKC8Lnb+DjDLYu30OdsHwb1G8TIT0eSlpbGhg0b6NatmzVLtYgEvRDCZri5uREVFUVUVBTTpk1j3bp1BAYGmufFxcUB8Prrr5vnBQYGMmnSpFKtIzsz+6Z5lZyMo2p69O3B8ePH+fDDD3n99ddLdb9lRb6MFULYjGrVqrFu3Trz9Lp16+jatStTp069br1x48aZXzdu3JiePXuWah1HVh8BQDkaL366cOEClzMvA7Br1y78/Pw4cOAAH3zwAR988AF//vknffr04fvvvy/VOkqLBL0QwmacPn2ayMhIfH19mThxIgArVqygXbt2hIeHM27cONzc3Mzrb9++nYCAAKpVq1ZqNZw7eI4N726g/kP1adTP+NzWxMREHuv3GE6uTlRbUY0vv/ySJk2amLepU6eOzYY8SNeNEMIGZJ3NIutsFseOHWP9+vU899xzPPPMMzRv3pyDBw+yceNGKleuzEcffXTddrNnz6Z///6lVofWmmVDl+Ho6njd7Qwa1WnEM5ee4avXvmLDxg3XhTxQqk+DKgsS9EIIq8g+n82umbuYHTObj+76iInBEzn3+zkAOnfuzPHjx/H09MRgMN4Bsn///lz7QKK8vDwWL15Mr169bquOJYOX8I56h7SjacT+N5aja44S/X40nkGe5nWStyaj8zQhD4QU8U62S7puhBDlKutcFuP9xpunvWp50fIfLdn+7XbmPjSXwZsHk+aRhp+fHxkZGVSpUgWANWvWUL9+ffN2v/76KxEREVSuXLlE+9dac+ncJdKOpJF2JI0d03YA8FnIZwDc3fluIp67/kFNJ/84iXJQBDUPuqVjtrZig14pZQA2AK6m9edrrUcrpR4DxgANgBZa6wKf/aeUOgacB/KA3MIedSWEuDNc+7i9Rv0bUSuqFonzEzl88jBLWcrifoupUqMKX375JXPmzGH69Om4u7vj5+fH9OnTzdvOnj2bAQMGWLzfjKQMZneezdm9Z4tc76/dfzGn2xxqtq9Jw14N8avvx5ndZ/Cp43Nd7RVJsc+MVcZOKg+t9QWllDPwG/AykAHkA18CrxUT9BFa66Jb9xryzFgh7FvigkQW9F1Afq7xvjHeId40eLQBob1DCWwWWCa3+s08mcnHwR/fNL/how1JnJ8I/D3KRufdnIsNH23IY/MeK/W6SsttPTNWGz8JLpgmnU0/Wmu91/TmpVWnEOIO0bBXQ+pm1GXH9B1Ub12du5reVaZZovONX7Je1W1KN0IfC8XJ4ISTwYmfnvuJ7VO2Ezk6kvZvtSftSBr7f9zP/qX7Ob7uOC6eLkR/EF1m9ZU1i76MVUo5KqV2AmeAVVrrrSXYhwZWKqXilFJDitjHEKVUrFIqNiUlpQRvL4SoiJzdnWnxYosyO4O/VkpiCgeWHgCg5YiWNH+2OQYvA04GJ/7a/Rfbv9pO4ycaE/NBDB06dKDnMz2J94gnySmJj/iIpfWX0uvZXuYLtmJjY2nZsiWRkZF06dKF8+fPl2n9t6vYrpvrVlbKC1gEDNdaJ5jmraPorpsgrfUppVQAsMq07Yai9iNdN0KI0paRlAFAlepVzPMuZ1xm1gOzSDucxvCDw2nUohE7Nu7gj8//IHFeInEH40hplsKyuGXXvdejjz7K8OHDiYyMZMyYMVStWpVhw4aV6/HcqKiumxINr9RapwPrgJgSbHPK9PsMxg+JFiXZpxBClIYq1atcF/IXUy4yM3omp3ecpvvU7rj5uHH69Gki7ongxQ9eJKtSFh3HdmT3md20a9eO4cOHc+nSJQBCQ0NJT08HIC0tjYCAgIJ2aTOKDXqllL/pTB6llBvQEdhnyZsrpTyUUp5XXwOdgISitxJCiLKltebb7t+SkphC38V9afBIAwC+fvJr+mX2Y/iI4az0XkmP53sUeMFWr169eOmllwgLC2Pbtm306NHDmodTLEvO6AOBtUqp3cA2jH30S5VSjyilkoFWwDKl1AowdtUopX42bVsV+E0ptQv4A1imtV5e+ochhBCWyTqXxYK+Czi59SRt3mhD3S51AWPXTuJ/E2nyZBOGfTSsyAu2hg4dysKFC0lISKB79+58/PHNo3lsiSWjbnYDTQuYvwhjV8yN808BXUyvjwBNblxHCCGsIScrh2+7fcupuFNEDIug5SstAUg9lMqsXrNQzor2/9ee+Pj4Ii/Y0lrj7+8PQEBAgM3fAkGujBVC3DHWv7ue5K3J9F7Q29xdc3b/Wb645wuSSWZTyCZWP70apVSRF2yNHTuW3r17YzAYcHBwYPbs2dY8rGJJ0Ash7hiJ8xKpXK0y9brVM89zdnfGwcmB4Nxg1v+0Hv+G/uZlTZo04fnnn7/pfSIjI9myZUu51Fwa5KZmQgi7p/M183rPI+1wGpnJmWz/art5WZXqVRh+cDgGbwNr/2+tFassOxL0Qgi7d/7P8yTOM97moPGAxtSJqXPdcq9aXnjV8jI/K9beSNALIeze1XvcRDwfwcMzHsY7xPu65clbkzm94zQ12tWwRnllToJeCGH3un/VHYDYybFMvW/qTWfuC/stBCBiqH3eXFeCXghh95oNbsZbV96iUb9GnIo9xdn9199M1+BtoHZ0bdz93K1UYdmSUTdCiDvCr//6lfi58QB8FfHVTcu97/a+aZ69kKAXQtwR9i023rnFq7YX7n7uaK1BGy9+Sj2YipOb/cah/R6ZEEJco26Xuvwx6Q8yjmfw5Oonr/tCdmbHmWRnZFuxurIlffRCiDvCg589iG99X7TWVAqsZJ5//s/zJG1KIrh1sBWrK1sS9EKIO8KF0xfISsmieuvqOLs5A3DlwhW+7fYtylHd9EBweyJBL4Swe507dyaweiCrzq+iw7sdWL9+PW3atKHp3U35cPuHtP2i7XW3Phg4cCAdO3Y0Tz/xxBNERUURERFh83eqLIj00Qsh7E5GUga/vPgLJzadID83nybZTXDLdcP/AX9qd6hNtSvVmDRoEj89+xNZD2fxU8JPtKc9APHx8eaHilw1bdo0XFxcyM3NpUGDBgwePBhPT08rHNmtkaAXQtiV5C3JfNfjO3KycgjtG4qLhwsOTg44HXPCtaErAFl/ZrHqn6uo1aEWqe1T8fPzM2//73//m5EjRzJq1CjzPBcXFwAuX75MjRo1cHevWOPtJeiFEHbj6JqjzOkyh8rBlXlq/VP43fN3gJ+cfpJjh46xf8l+fhn+C3uv7OXbM9+S9d8sfv7Z+KykdevWUa9ePapWrXrTez/22GOsX7+eYcOG4ejoWG7HVBok6IUQdmPx04vxCPBg8JbBuPu5k5+bz/y+8zm65ihb0reQoTNwwIFKgZX48LcPCWwayA8//MDIkSP54YcfGDt2LN99991NXTcA8+bNIysri/bt29OnTx8aNmxY/gd4iyTohRAVXn5uPg5ODrj7upN5MhMng5N5/oGfDpB3JY+63eqSnpfOEyOeICAigEpexiGWXl5euLu7c/78eU6fPk3fvn25dOkSe/bs4f3332fkyJHk5OTg4uKCwWDAzc0NNzc3ax5uiUnQCyEqtB3Td7DkmSUYvA1cTrsMwJ87/qRmu5o4GZyodl81Pv/tcy4fuUx2djZnPj9Dt27dmDVrFg4ODri4uDBlyhQ8PT3ZuXMnAMeOHWPw4MGMGjWKnJwcOnXqBEB2djZ9+vShdu3a1jrcW6K01tau4SYRERH66kN4hRCiMKmHU5nSfAo5F3NoNqQZ55PPU7l6ZTqO7YhLJeMXqLvn7GbRgEUM2T6EwKaBVq647Cil4rTWBV4MIGf0QogK68DSA2RnZDMsYRgBoQEFrnMm/gwArpVdy7M0myJBL4SosBxdjKNfDFUMNy3LPp/Nhvc2sHn8ZsKfCsfnbp/yLs9mSNALISqspE1JGLwNeAR4AJBzKYdf3/yVU7GnOLXtFHlX8mj6TFMenPSglSu1Lgl6IUSFlJOVw74f99GoXyPzmf2OaTvY+ulWqrepzr0v3kto71CC77Pfm5VZqtigV0oZgA2Aq2n9+Vrr0Uqpx4AxQAOghda6wG9PlVIxwKeAIzBVaz22lGoXQtzBVr62kpyLOTR89O/x7Pt+3IdnkCeDNg5CKWXF6myLJTc1ywaitdZNgHAgRinVEkgAemL8ECiQUsoR+AJ4EGgIPK6UqjhXGQghbFbsf43nlu7+f9+OILR3KOdPnWfqfVM5tOIQtjiq0BqKDXptdME06Wz60VrrvVrr/cVs3gI4pLU+orW+AnwH9LitioUQAnhs/mMATGk2hU3jN5G4IBGDt4G6Xetyatsp5sTM4ePgj7l49qKVK7U+i/roTWfmcUAd4Aut9VYL378akHTNdDJwXyH7GAIMAahRo4aFby+EuFOtfmP1369fX13gOudPnWfeo/N4at1T5VSVbbIo6LXWeUC4UsoLWKSUCtNaJ1iwaUGdZAX+v5TWegowBYwXTFlSlxDiznX/h/czv/d83HzdCB8U/veC/L9f/j7xd5xcZcxJiVpAa52ulFoHxGDsoy9OMlD9mulg4FRJ9imEEAUJfSyUXV12cXbfWTqN73TdMq01O6btACC4lYy6KbaPXinlbzqTRynlBnQE9ln4/tuAukqp2kopF6AvsOQWaxVCiOt4VPUg70qeeVprzf4l+5kTM4efnv2J2vfXps3rbaxYoW2wZNRNILBWKbUbY3Cv0lovVUo9opRKBloBy5RSKwCUUkFKqZ8BtNa5wIvACmAv8IPWek9ZHIgQ4s6Tl52Ho6txDH3nzp3x9/VnWI9hnN51moZvNuTTtE/xDvDmt99+M29z4MABoqKiiIqKYsSIEeaRORX9cYFF0lrb3E/z5s21EEIU59se3+rPG3yutdY6KSlJj/3HWN2BDvrY+mP64sWL+ty5c3rgwIF648aN5m169Oihf//9d6211kOHDtUrV67UWmudnZ2ttdY6JydH16lTR2dmZpbz0dweIFYXkqnycHAhRIWUdS6LwysPm/vgg4ODSTuShlKKu5rehbu7Oz4+N9/f5sCBA0REGG/y2KJFC9auXQtU/McFFkWCXghRIcX+L5bcS7m0GtHKPO9M/Bk8Ajxw9Sz8TpWNGjVi+fLlaK1Zvnw5qamp5mWPPfYYISEhtG3btsI9LrAoEvRCiAonPy+fuC/jCHkgxHx74hObTpB6JJXAiKLvOT9hwgSmTZvGAw88gLe3N0FBQeZl8+bN49ixYyxbtozExMQyPYbyJEEvhKhwDq88TGZSJs2HNDfP2/n1TpxcnQhqHlTElsYunkWLFrFq1SouXrxIz5490Vpz5coVgAr7uMCiyJUEQogKJS8nj43vb8QzyJP6D9UHjP317854l9Nup9k5Zyc7du1g5syZ9OzZk8TERPbs2UOXLl145513mDt3Ll999RVKKZ544gnCwsLs4nGBRZFHCQohKgytNfP7zCdxXiLdp3an2TPNAFgxYgVbPtnCsPjCnzRl74p6lKB03QghKoy9C/aSOC+RDu91MIf8vh/3seXjLTR/rvkdG/LFkaAXQlQIWmvWjV5HQKMA2r7RFoBLaZdYPGgxQfcGEfNJjJUrtF0S9EKICuHEbydISUyh6dNNcXAyRte2L7ZxOf0yjfo3wtHZfoZDljYJeiFEhbDt820AuPn+PRomP894q8oVr6xgUr1JHFpxyCq12ToJeiFEhdBkYBPAeH+bq6JGR/GvjH/Rc05PnAxOzO06l8QF9jP+vbRI0AshKoS7O9+NXwM/Nry7gcyTmYBxqCVAvW71GLBiAN61vVn+8nJ5hOANZBy9EKJCcHB0oMf0HnwT+Q2f1PgE7xBvUg+lFrju+ZPnqRxcuZwrtF0S9EKICiO4ZTDDEoaxa8YuUg+lclfTuzB4G/Cr72dex83XjUqBlaxYpe2RoBdCVCi+dX2Jfi/a2mVUKNJHL4QQdk6CXggh7JwEvRBC2DkJeiGEsHMS9EIIYeck6IUQws5J0AshhJ2ToBdCCDtnk0+YUkqlAMctXN0POFuG5ZSWilBnRagRKkadUmPpqQh12kKNNbXW/gUtsMmgLwmlVGxhj8+yJRWhzopQI1SMOqXG0lMR6rT1GqXrRggh7JwEvRBC2Dl7CPop1i7AQhWhzopQI1SMOqXG0lMR6rTpGit8H70QQoii2cMZvRBCiCJI0AshhJ2rEEGvlApXSm1RSu1USsUqpVrcsLyGUuqCUuq1QrYfo5Q6adp+p1Kqiw3W6KOUWqWUOmj67V3aNRZVp1KqxTXts0sp9Ugh21utLUtQo7Xb8gGlVJxSKt70u8CnZFi5LS2t0dpt6auUWmv6b+fzIra3ZltaWmO5tGWBtNY2/wOsBB40ve4CrLth+QJgHvBaIduPKWyZDdU4DviX6fW/gP+UZ52AO+Bkeh0InLk6bSttWYIard2WTYEg0+sw4KSt/V2WoEZrt6UH0BYYCnxexPbWbEtLayyXtizop0Kc0QMauPqk3yrAqasLlFIPA0eAPeVf1nVut8YewAzT6xnAw6VeoVGBdWqts7TWuab5BtN61nK7NVq7LXdora/+++8BDEop1zKqoTi3W6O12/Ki1vo34HIZ7bckbrfG8mrLm5XXJ8ptfpI2AE4AScBJjJf6gvGT9HegEkV8opuWHQN2A9MBbxusMf2G6bTybEvTsvsw/kd/AXjE1tqyBDVavS2vWedRYLUttqWFNdpEWwJPUfwZvVXb0oIay6UtC9x3ee3IgkZcDSQU8NMD+AzoZVqv99U/SuAjoPc1/9CFhWhVwBHjdxLvA9NtsMZS+yO4lToL+IP+AzDYUluWoEabaEsgFDgM3G1rf5clqNFW2vIpig5RW2jL4mostbYs8XGV145uq0jI4O8x/wrINL3eiPFT/BiQDqQCLxbzXrWABFurEdgPBJpeBwL7y7MtC1hvLRBhS21paY220JZAMHAAaGPhe5V7W1pSoy20pWlekSFq7ba0pMbyasuCfipKH/0pINL0Oho4CKC1bqe1rqW1rgV8Anygtb7pW2+lVOA1k49g/IS2qRqBJcBA0+uBwOIyqLHQOpVStZVSTqbXNYH6GD+crmPNtrS0Rqzfll7AMuBNrfWmwja2cltaVCNWbktLWbMtS6C82vJm5fWJcpufpG2BOGAXsBVoXsA6Y7imWwSYiulsD5gFxGPsv1uC6VPVxmr0BX7F+MfzK+BTnm0JPIGx73snsB142NbasgQ1Wrst3wIumuq8+hNgY21paY1WbUvTsmMY/0/4ApAMNLSltixBjeXSlgX9yC0QhBDCzlWUrhshhBC3SIJeCCHsnAS9EELYOQl6IYSwcxL0Qghh5yTohRDCzknQCyGEnft/6TEGicCEo+wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 5\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        if 0 == 0 :  # y <38.8:  #only eliminate lake tracts south of Chesapeake Bay Bridge from map\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "            # isSkippedTract[m] = 1   #not yet\n",
    "            if lakeTracts == [-99999]:\n",
    "                lakeTracts = [m]\n",
    "            else:\n",
    "                lakeTracts.append(m)\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oceanTracts = [1193,1343,558,54,578]\n",
    "for t in oceanTracts:\n",
    "    x,y = tractGeom[t].exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    isSkippedTract[t] = 1\n",
    "                \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2796 5\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baabb633-83ac-4315-92bd-1d6fdd9e999b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, manually flag tracts that should be excluded from the map, such as lakeshore empty tracts\n",
    "isSkippedTract = [0]*nTracts\n",
    "#skipList = [1108,2676]   #Lake Erie and northeasternmost Susquehanna.  All others are small.\n",
    "for s in skipList:\n",
    "    isSkippedTract[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>CTYSOSID</th>\n",
       "      <th>PRECINCT_I</th>\n",
       "      <th>PRECINCT_N</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>CTYNUMBER</th>\n",
       "      <th>CTYNUMBER2</th>\n",
       "      <th>FIPS2</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>...</th>\n",
       "      <th>G20PSCRMCD</th>\n",
       "      <th>G20PSCDBLA</th>\n",
       "      <th>G20PSCLWIL</th>\n",
       "      <th>R21USSRPER</th>\n",
       "      <th>R21USSDOSS</th>\n",
       "      <th>R21USSRLOE</th>\n",
       "      <th>R21USSDWAR</th>\n",
       "      <th>R21PSCRMCD</th>\n",
       "      <th>R21PSCDBLA</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215122</td>\n",
       "      <td>215122</td>\n",
       "      <td>122</td>\n",
       "      <td>FIRST AFRICAN</td>\n",
       "      <td>MUSCOGEE</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>215</td>\n",
       "      <td>238</td>\n",
       "      <td>668</td>\n",
       "      <td>...</td>\n",
       "      <td>251</td>\n",
       "      <td>587</td>\n",
       "      <td>44</td>\n",
       "      <td>230</td>\n",
       "      <td>589</td>\n",
       "      <td>222</td>\n",
       "      <td>599</td>\n",
       "      <td>239</td>\n",
       "      <td>564</td>\n",
       "      <td>POLYGON ((-84.96984 32.46725, -84.97031 32.467...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215108</td>\n",
       "      <td>215108</td>\n",
       "      <td>108</td>\n",
       "      <td>ST MARK/HEIFERHORN</td>\n",
       "      <td>MUSCOGEE</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>215</td>\n",
       "      <td>3243</td>\n",
       "      <td>1676</td>\n",
       "      <td>...</td>\n",
       "      <td>3268</td>\n",
       "      <td>1456</td>\n",
       "      <td>122</td>\n",
       "      <td>3071</td>\n",
       "      <td>1484</td>\n",
       "      <td>3055</td>\n",
       "      <td>1499</td>\n",
       "      <td>3112</td>\n",
       "      <td>1397</td>\n",
       "      <td>POLYGON ((-84.96552 32.53259, -84.96852 32.532...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>057031</td>\n",
       "      <td>057031</td>\n",
       "      <td>031</td>\n",
       "      <td>R T JONES</td>\n",
       "      <td>CHEROKEE</td>\n",
       "      <td>28</td>\n",
       "      <td>028</td>\n",
       "      <td>057</td>\n",
       "      <td>1021</td>\n",
       "      <td>513</td>\n",
       "      <td>...</td>\n",
       "      <td>998</td>\n",
       "      <td>461</td>\n",
       "      <td>60</td>\n",
       "      <td>891</td>\n",
       "      <td>455</td>\n",
       "      <td>879</td>\n",
       "      <td>467</td>\n",
       "      <td>902</td>\n",
       "      <td>434</td>\n",
       "      <td>POLYGON ((-84.46579 34.25122, -84.46545 34.251...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>057033</td>\n",
       "      <td>057033</td>\n",
       "      <td>033</td>\n",
       "      <td>SALACOA</td>\n",
       "      <td>CHEROKEE</td>\n",
       "      <td>28</td>\n",
       "      <td>028</td>\n",
       "      <td>057</td>\n",
       "      <td>454</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>419</td>\n",
       "      <td>61</td>\n",
       "      <td>17</td>\n",
       "      <td>419</td>\n",
       "      <td>69</td>\n",
       "      <td>421</td>\n",
       "      <td>68</td>\n",
       "      <td>416</td>\n",
       "      <td>64</td>\n",
       "      <td>POLYGON ((-84.53036 34.38103, -84.53047 34.380...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01506</td>\n",
       "      <td>01506</td>\n",
       "      <td>06</td>\n",
       "      <td>CENTER</td>\n",
       "      <td>BARTOW</td>\n",
       "      <td>8</td>\n",
       "      <td>008</td>\n",
       "      <td>015</td>\n",
       "      <td>2312</td>\n",
       "      <td>568</td>\n",
       "      <td>...</td>\n",
       "      <td>2230</td>\n",
       "      <td>516</td>\n",
       "      <td>107</td>\n",
       "      <td>2026</td>\n",
       "      <td>507</td>\n",
       "      <td>2019</td>\n",
       "      <td>511</td>\n",
       "      <td>2032</td>\n",
       "      <td>478</td>\n",
       "      <td>MULTIPOLYGON (((-84.65788 34.14247, -84.65830 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  DISTRICT CTYSOSID PRECINCT_I          PRECINCT_N   CTYNAME CTYNUMBER  \\\n",
       "0   215122   215122        122       FIRST AFRICAN  MUSCOGEE       106   \n",
       "1   215108   215108        108  ST MARK/HEIFERHORN  MUSCOGEE       106   \n",
       "2   057031   057031        031           R T JONES  CHEROKEE        28   \n",
       "3   057033   057033        033             SALACOA  CHEROKEE        28   \n",
       "4    01506    01506         06              CENTER    BARTOW         8   \n",
       "\n",
       "  CTYNUMBER2 FIPS2  G20PRERTRU  G20PREDBID  ...  G20PSCRMCD  G20PSCDBLA  \\\n",
       "0        106   215         238         668  ...         251         587   \n",
       "1        106   215        3243        1676  ...        3268        1456   \n",
       "2        028   057        1021         513  ...         998         461   \n",
       "3        028   057         454          69  ...         419          61   \n",
       "4        008   015        2312         568  ...        2230         516   \n",
       "\n",
       "   G20PSCLWIL  R21USSRPER  R21USSDOSS  R21USSRLOE  R21USSDWAR  R21PSCRMCD  \\\n",
       "0          44         230         589         222         599         239   \n",
       "1         122        3071        1484        3055        1499        3112   \n",
       "2          60         891         455         879         467         902   \n",
       "3          17         419          69         421          68         416   \n",
       "4         107        2026         507        2019         511        2032   \n",
       "\n",
       "   R21PSCDBLA                                           geometry  \n",
       "0         564  POLYGON ((-84.96984 32.46725, -84.97031 32.467...  \n",
       "1        1397  POLYGON ((-84.96552 32.53259, -84.96852 32.532...  \n",
       "2         434  POLYGON ((-84.46579 34.25122, -84.46545 34.251...  \n",
       "3          64  POLYGON ((-84.53036 34.38103, -84.53047 34.380...  \n",
       "4         478  MULTIPOLYGON (((-84.65788 34.14247, -84.65830 ...  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/ga_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2679 0.498716661561674 4936344 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic GA map\n",
    "#tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        #tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-81.15,32.2)\n",
    "pt2 = Point(-81.15,31.5)\n",
    "pt3 = Point(-80.8,31.5)\n",
    "pt4 = Point(-80.8,32.2)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "#wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  59 tracts w pop 173372.0  to reassign to tracts...\n",
      "[230, 235, 240, 440, 442, 565, 1135, 1139, 1370, 1520, 2632]\n",
      "I have flagged  69 precincts to reassign to precincts...\n",
      "[661, 666, 789, 872, 876, 1321, 1323, 1682, 2247, 2399]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.05 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.05 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  173372.0 people (VAP of 140039.0 ) and  85503.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for western MD\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-80,40)\n",
    "pt2 = Point(-80,39)\n",
    "pt3 = Point(-78,39)\n",
    "pt4 = Point(-77.5,40)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  149 tracts w pop 201388.0  to reassign to tracts...\n",
      "[557, 558, 564, 565, 566, 568, 569, 570, 573, 575, 576, 585, 664, 668, 818, 825, 826, 1524, 1525, 2522, 2524, 2528, 2981, 2984]\n",
      "I have flagged  92 precincts to reassign to precincts...\n",
      "[649, 650, 652, 658, 664, 670, 672, 673, 675, 676, 681, 690]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  201388.0 people (VAP of 159890.0 ) and  89090.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "if type(tractMAP) != type(tractGeom[1]):\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "else:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4538dfc-7d35-46b4-95c1-c5d93169e971",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.26269315673289184 906\n",
      "100 0.5349274583557228 3722\n",
      "200 0.7605144247480013 2877\n",
      "300 0.4270504731861199 2536\n",
      "400 0.9177215189873418 1264\n",
      "500 0.7812692544670363 1623\n",
      "600 0.09090909090909091 143\n",
      "700 0.42857142857142855 3465\n",
      "800 0.16117435590173756 1669\n",
      "900 0.06277056277056277 462\n",
      "1000 0.0 0\n",
      "1100 0.77130476649013 2077\n",
      "1200 0.5963302752293578 218\n",
      "1300 0.0 0\n",
      "1400 0.48002421307506055 1652\n",
      "1500 0.9306122448979591 490\n",
      "1600 0.0 0\n",
      "1700 0.03208852005532503 3615\n",
      "1800 0.5814713896457766 1835\n",
      "1900 0.690282131661442 1595\n",
      "2000 0.16943709860219117 5294\n",
      "2100 0.6050995024875622 1608\n",
      "2200 0.9320987654320988 162\n",
      "2300 0.8849693251533742 1304\n",
      "2400 0.21965952773201539 1821\n",
      "2500 0.19631901840490798 4401\n",
      "2600 0.5749625187406296 1334\n"
     ]
    },
    {
     "data": {
      "image/png": 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8aRFbDp7m4auGktohTm/TFAbFl+a9GpgmpRwOjABmCSEuBBBCpAGXAAc0szAApg5KYU/ZOQ6UVzZdabTMBE97qqvhxRebZGu4R4i6vy1han6k6ObczdiqbEi7RNolJpOp/hsWYI41MzxnOOBsH/711DF+OHYfPUxHEdjpSzEmHA0EurnzI5x/EpKCMMGvlqNYw7Cq5LCeHXn3Z9k8ePlglmw7yg/++Y0acq/wSqseuHTePRWut1bXf/cd9STwP8CHmlgXIFMzU/jT4u3kFZVy80XpDVcazStr6Ao7/3s87rtDIrOemkXlxkISju6jsltf0nMmNfG+N722qe6bMVlNzH56NpXllQ3i1gArF64kISmBbfcuYWiNnaFmE5sYRjfHYQRNGwj3CNXNuZvZ+Iozni7tks9++RkpWSmBe+G+PA21Ndxl5AyhFrh9Yj+EECxYvJ2/Li3if2YOQgiht1kKg+FTDFwIYQbWAxnAc1LKNUKIK4FDUsrNLd1YQog5wByA3r17t91iH+jbtR3pSQnNC7jRslLc9uTmwmuvOT1xV8PiGRJJFaUMlttIkXtJiy2DnOXgkVNSnF9cHzIRkDE7o0ln4/qX1vPJXZ8gHRJhEk7PzgFCOhghvkMKicliYuRPRzI8Z3gDYXaHUjzDMvba+jBKczrrbny8dni2FqM2WrgrxNx0YR8WLN7O8/l7uDija0R1yiuCg08CLqW0AyOEEJ2AD4QQFwB/AC71Yd+XgJfAmYUSuKn+MWVQCu+sPcD5GjvxMeaGK43mlbnsOTZyJvtfXUZ5jyyG0atBdcRSunCMSaxkAjnVb5LWSOzcYQ53/Hvnxzsp+rAIk8XEZc9eRikprLnzU5CSg/Si2J5OX9N+0swH6+Pl0hnfLqEXW/LTmELDj6nuHNXOiTPMVqeX3pzO9qK+8TGZTYy4bUSTRqHVp6Ew6oTUghiLic9/PYkbXl7Nb97dzK0T0qm1O5g1rDsZKYl6m6cwAH6nEQoh5uPszvol4A4y9wIOA+OklF7nNgtFGqGbFTvLyHltLa/fMpapmSkhOWdbKCkoYdGURc7OQpzx6tlPz3ZmklTZnKEVBAI7Uy2rmLji0SZi5k4b3PDyBhx2icAZUTlr7sQGRjLJnscherGIHOyYsZocPDVnK+NG2lly7xLsNXYOmXuTK3KotZmadXrd5wDqBHnhQpj3oMTuEJhNkgWPCC5mJXnz8pB21/0lwBJnaZq50lKIJMo9cDfr95/krrfXc+xMNQBCwJ2T+3P/pYMwmVRYJRoIOI1QCJEM1EopTwkh4oEZwGNSyhSPbYqBMUbIQnEzrm8X4q1mviwsDQsBL84vxu4xJZy9xk5leWVd7HnT65tw1Noxm82kP3t/3QQJjbXv5N6TlNs70Z6zmLDjwMw+exqJnMGBmb2kY8eMxIQdwaneIxg9B1KyUliRW8yqDVnUrDPhcDTv9DZX7Gtw0jFMji44MGFyOBicdIL0LKe37mx8AIn/mStGC3fpxOg+nVk9dzpnqmzU2Bw8sbSI5/P3sH7/Se6fOYixAVbfVIQ/voRQugOLXHFwE/CulHKxtma1nTirmQkZSeQVlSKlNHwHUPqUdMxWc70H7upEdAvm8JzhDeLJbue0uhpMJlhw3wnsz+Rir7bTEcGnzCae85wnjstYgkDiwEStiMWExC4lJrOoi1ocJI07FqVRXQ0Oh/OYrfXxuhuQTgeOcLPpE/Y5+tDXtJ+k8gGkZU9s2PjYHE0zZ3zxsI0W7tIJIQQd460A/OWaLEb16cTjS3fywxcKmJCRRE52OiPSOhEfY6ZDnFVnaxWhwpcslO+Aka1skx4sg4LJlEHOWXr2lFWQkdJeb3NaJC07jZvzb24SnvBcD9Sl9uXn14utwwEPPtGJW+lOL0cJZiSJopKVTORiVmLGmd1iEg4mTZSsWuHsE6ithS1bnProDje7xXvGDHjooaZ9im5nGOq112q5gBzLJibZv3aJ9CV1NjfX+NQRihi30QZtBQEhBNeN7c2Vw3vy5upiXltVzM/eXO9a50yjvf3ivozr20UNBIpwIm4kpifu0EleYZnhBRxarkXeeL7OoU/djsmUisM9uAZBsUinJyU4MJEozmISkn32vtSyEqvJjiXWzDdl6Q2O++qrMGcOJHnUULJYmhdvT2d55sz6zEcw0emOq5nae0uzGSder0vrlM4Ij6HHx5iZM6k/t03oy9d7yjl4spKDJ8/z7rcl3PDKGhJjLVw9sgc3Z6czINX497/CfyJawHt2imdQanvyikq5Y1I/vc3xCW+pd43n60wq38l996XyxBNOEY2NFQwb24G8FVPYR18OOXriQFBCGovIoT/FXPvLdJb/raGQbtgA3/8+fPKJ0xEGtyg3pPF4o48/rt/ObIarczqRnT3Rv4vVOsYdJVksFrOJyQPrZ/b51fQBfL79GPmFpby77iBvrznAj8emcd8lg9RkyhFGRAs4wJTMZF5btY+zVbW0DzQ26O0xPMiP5429bM+MDc80QXOMmfKkgTyzwLmfyQRjx8IHO7PYjxVnV0V9vvZB0jhMGufz60Xajc0G//1v02WNtc7TWTaZ6o8jBNx2m5fL9+XzCWaMu/H5jDZoy18CvL/irGauHN6DK4f34MErhvD3L4p4a/UBVu0+zis5YxnUTXnjkULEC/jUQSm8+NVevt59nFnDuvt/AG+P4V6Wtzp4pQUae9meGRuN5+t8Kz+1LmYNsGIFOMvVuFI+nGMq644thNNUX7Bam2qdp7OclAT33gvJ1SX0NxVz5ch0oNG1hjp84e184ZrFEqTPr0u7GB65Oos4i5m31uxn1j9WMG1QCvfOGEhWr44aGK4IJREv4KP7dKZ9rIW8wrLABNzbY3gzy0vo5dWD9oXGXnbjWieeseQpNByBX0/z2TZ2O2za5P3cVitcfjl06+a9rKyns5xGCWvvzgW7nQ33mhmWlRP6DkpPvJ0vXLNYgvz5PXjFEH4xNYPXvynm5RV7WX9gDcvvm0xSogqphDMR30VtNZuYOLBrXTqh33ibkqyZ5c150M1RUlDCyoUrKSkoAerrOR3E6WVPXTC1VfF3z1VgCtI3+NOfwgcfwPPP+6YTieXFCIcdHF6uVcup3JorgBXKqeNCgQbX07ldDPddMpB/z7mQU5W1vLvuYJuPqdCXiPfAwZlO+OmWo2w/coahPfx8bPT2GN7M8nRKWvSgoflskhvuTfV4Uk5j4lzfvPby8uY7HN0IAX36wKFDTkfOnSJotTrf21ylwmNjnV63P7T2tKBZ+MJbaCGcwyXNoeH1DE/rRHpSAk8t28mo3p3UNG5hTJQIuLOHPr+ozH8BB++P4Y2WN45TN+dBN/bSP3u/0ucn5eb66Eym+ji4GyEgMxP27oWSEmda4E9/CiNHOkXf7czl5jr/jhxZX1XX13P7cq2ahC9aCi2Ea7jEGxpez8s5Y5jz5np+8uoanrl+FLOGqWncwpGoEPCU9nFk9exIXmEpv5iaEdhBfMwIaCmXG5p6rrOvSeDNla0nSjQeefncc5CVBVdc4Uzpczic3rjJ5PSoJ0+GnTvrs0V693bme3teitvr9qWia3PbuK+1oADeWtjwo9Fs/Ey4Z5YYhAGp7fnvLybwvWdWsWDxdiXgYUpUCDjA1EHJPJu3m5PnaujcLsa/nT0UrMScTvFtDzepx+0rTT3XVJZntS52+fk0GHl5111OsbbZnB73VVfB7NmwcaNz+5Ejm9e5xmJ8882t95W15PQ2J+6gYQJKpIVKtMDH1rNjvJXrx/XmsSWF/OrfG7l6ZE/GpXehXWzUyELYEzXf1JTMFJ7+cjcrdpVx1Yie/u3sUrASe3dy7T/G/uJuzIuK/c4ycdPYS/flSblxuMQ9lyU4Pe8PP4SBA+H11+uF8+mn60Mm7uM3FmNo3aFtyen1NsGRpgkokRYqCSZ+ph/eOiGdM1W1vPF1MR9uOkz3jnFc1lWyb38ZozJSuf3GycRZzV73V+hLxGehuBneqxNd2sWQX1Tm/84uBSsW/ZyV/Dwq64WK7Gxn2MTs5bckJTz+uNNLl9L5d+NGmDu3+QE57uSGnJzWZx1raWay5pIlDJMQouV0bUbFzykD46xmfjcrk3UPzuDFm0bT+fwZXt1Tzd6yCp4oPM/w+Uv48UsFvLZqHyfO+TiQQBEyosYDN5sEkwcm89XOMuwOidmfOsouBUvP/RLz6w7sNuk1y8Rf/Bn4k5Xl7JBsPJrSTeOMlKPNVGZvKammJVrqx23ueLpHOSK8DopXAuwjaBdrYeaZfcxc+CMcNjsmJKt7Z/HBxdezvrY/f9p7gldX7ePhK4cyfXCK4at7RgtRI+DgzEb5YOMhvjt4ipG9O/u3c3Y2adnZ5OQEPtKyMS0NnW+O/Pz61D9wxr49f0dWq3O9W+A/+8ypYwFXaPUxltrc8XSPckRJHZQmtKWPID8fHA5MSCQw7sBWxvxrHiYcrE0fzv23/pnbc9cxcUBXnrl+JJ0S/OxLUgSdqBLwSQOSMQnIKyxtKOB+pEy0lmXiDy0NnW8OT+fKbHbWIHFnkrjNz811TmwvpVPMc3MD9ITD3YON5myVQFrPggI4cMDpBdTWIhEUOgaQSREmJGOLNzPv06/49+SryN9ZxnWP5TN/5iV89ZVQfck6ElUC3rldDCN7dyavqIz7Lh3kXOirUGmQF9fiYJhmzteSc+X5etEi5+VYLPDKK04n1Gr10wn18GBldQ3FD72B5aFeQWu8NCfCs1WCejt6/gbMZpgzh7KRM1l91ycMsO9GYseOmQ/W9OHNNWOYMuY9iqZ34Ee5eVQf6sLj73fh7cdTmT1VDcsPNVEl4OBMJ3zi852Unq0ipX2cb4/aGnmjXgfDtHC+xs5V4xi6p26tXVtfabCmxumNt2a2WxiuSJpCVkwMsroGm8NE3jIHR1bmBpx5owu6x3G0IeDb0Zvqe/4GAHr3JnXO1QwnjTfvgt72feyhP4u5AjsmHBuS6eqwc6BPe+L6HMc89BD3LN3Ou/3H+x+aVLSJqBPwKYNSeOLznXxVVMYPx6T59qitYTy12ZCMj96vtxi6W7d+/nP/bPEUhgUx2ax5ajmJ779B3jIHJY5eCH/ntDQyYTxTT0C3Y0uq7+U3MHrOaFKy/sj//rWYdz5KoK+jGDM2+jr2wwbYuuUSbA5Bx/F7aT+xkF//ZxP590/V7sIVTYiaNEI3Q3t0IKV9bH06YUs5cm6CkBfnV0ab63zSZK7zfnOn59YVv3LTWvGsnBznqEwhfKt30lgYFpdnY3noQY7EpiPMImiZN7rjFrN585x/wyzNMKDbsaX0whZ+AwdJ49nF6cx0LGEqedxifpvx4xz8/MUR5H1l4s755bSfWAhAcXkl56ptKEJH1HngQgimDkrh061HqLU7sJpNrT9qtzGe6vcjr+t8xQ+17P2mT0nHbBHOtEiLaCKu2dmQl+e72c05Yj7VPAk3wjxDpcXb0duTRWtPml5+A7m50NNWjBlnaqF0OMi8OpPRc0YDsFucY/F/ndtOHNCVeDXoJ6REnYADTM1M5j/rSli//yQX+lqJrQ3x1ID0Ijsby0O9OLIyF+GtPjgHyZGLKKYn6fIQacyg8cQK/pjtTRiCmXljCCIgQ6XZ77UlT6ENTkgx6c4BbNgxmervw5KCEr7+bGfddn/74XBM/oyvULSZqBTwCRldsZgEeUWlvgt4GwhUL1r1fvPzSbMXkyb3gN0cFE8yQvv9GhKpGSqteQoBfLk5OfD662nkVufQ31zM/N9UkJb/Fse2DCb33m1UZnSEy/oCMO7Py/ndrEx+PqV/8K5J0SIioEkOAmTMmDFy3bp1ITtfS1z/0mpOnKth6a8naXcSj8fZArKDrxfhnqttYNoyNZ5uaHQ/1GcmFZB1r/P4dpOFRfabOODoxfZxqayd2vAz6p/cjl/NGMiVw3u0+fwKEEKsl1KOabw8Kj1wgGmZKTz66Q4OnTpPz07xwT9Box9T9vLlZM8NsrhGqiepM/6OkDUMGt0PdY77wvw6D98koa9pPwdFGsO3lHPnw9N4ek8Zm0pOAbCn7Bz3vLORsemd6d5Rg9+XAohiAZ+amcyjn+4gv6iUG8f3Cf4JQtVRFhUxj9Di7whZQ6Hl/eARCxQxMQx56i4s5Ul1TynTGIjN7uCrnWX8dJHzSTt74Zdsnn8pHeOt2tgU5UStgPdPTqRX53jyCsu0EXCjd5SFcR601rQ6XVyk4+3eaOThp2Znk9poV4vZxPTBDZceO1OlBFwjojYGDjDvv1t5b/1BNv7xEm1qHhtVJFXsvFXCJgYe7HssSPfGZ1uO8PO3NwCQ2iGWn4zvw+fbj5HetR1PXTfCv2qgChUDb46pmcm8uXo/a/edYNLA5OCfwKjhDX/DO0ZtiDQkLFIntWiIgxT6m53Vnd2Pzubz7cd44P3v+NsXznTDLYdO07tLPPfPzGybnQogCkdiepLdryuxFhN5RaV6mxJa/BnKF+ajFiMaPydv8IkgzsZhMZvIqrDxZEw8r0yun4t2XfHJttupAKJcwONjzGT3Twpslp5wxpfyAW60EAmDUFJQwsqFK5uUKPAFQ0z2o8XUR/7cG63gzub5+o/5rLnmPe7IdMbG95RVsGJnGaEM30YqUR1CAZg6KIX5H21j3/Fz9O3aTm9ztKNxGMTX8I5WnbE6h2XakipomC4ErdJIgxT6a5zNM/XIeYb9eAR/XVJEzmtrGZvemSsu6MH4fl0YmNJejeIMACXgg1KYzzbyCkvpe3Ffvc3xTlsEry2Ko4VIGEABi/OLsVXbwAG2aptfqYKGKqVi1H4Wms/mmTiiJ7OGdeM/35bw8sq9zP9oGwAd462M79uFGUNScTgk/VMSGZveRecrMD5RL+C9kxLol9yOvKJSbjOqgLdV8NqqOMEWCQMoYEJSAjighF4UO9LpfSqZiT7ua/QMUaPgrRRErMVMTnY6OdnplJyoZO2+E3xbfIK8olI+336swTGW3TeJjJT2epgfFrQq4EKIOGAFEOva/j0p5XwhxALgKsABlAK3SCkPa2msVkwdlMKbBfuprLGREGPANq2tgmc0xdHZnoICeOP9LhxhFJ8xCztmVj0pybzat49VDYD1ndayedK6JJDWJYFrRvfCZnew9/g51u8/ydz/2wLAjL+vAOCJHw7nigu6a5PuG8a0mgcunNNPt5NSVgghrMAq4FfAdinlGdc29wBDpJR3tnQso+WBu/l693FufGUNr+SMYcaQxkMTWiYk+cLBCDkYLRVQJ3vqPspqZ2lUiUBiwmySLHhEMHduyExRtIDN7uDJZTt5Lm9Pg+UTB3TlhZ+Mpl2sAR0tDQk4D1w6Fb7C9dbq+i/d4u2iHRC2Xcpj0jvTLsZMXlGpXwIespoZwXD5jBYr1cmeuocZh8AkTJiEBCQxsUL3BxNFPRaziftnZnLfJYPYcOAkq3Yd5x/Ld7Fy13GGzl/Kf+ZcyPgQVBI1Oj41Y0IIM7AeyACek1KucS1/FMgBTgPNzqUkhJgDzAHo3bt3EEwOPrEWMxMyupJXWIqUEudDR+uEtGaG0QQ4TGkYvRE89ZSgvNw4DyaKhphNgrHpXRib3oWbsvsw5pFlACz4ZDuLf+lrr0Xk4pOASyntwAghRCfgAyHEMCnlVinlH4A/CCHmAncD85vZ9yXgJXCGUIJmeZCZmpnC59uPUXTsLJndOvi0T9TXzAhDVPw6MvjZJFVzHPzMQpFSnhJC5AOzgK0eq/4FfEIzAh4uTMtMAeDLwlKfBTzk040ZLY4dpujxMBM2tVUMjGcH5kX9VfgEfBiJKYRIdnneCCHigRlAoRBigMdmVwKFmlgYIlI7xJHVsyNf7vBvWH1adhoT504MjXirIe1hibuvJG9eXrOTUyt8IzHWwq0T0gEY/cgyio+f09cgA+DLUPruQJ4Q4jvgW+ALKeVi4C9CiK2u5ZfizEwJa6ZlprDhwElOnKvR25Sm5OdDdbUzlbC6OqKGtEc6zfWVKAJj/veGcqkr0WDKE/nsLj2rs0X60qqASym/k1KOlFJeIKUcJqX8k2v5Na73F0gpvyelPKS9udoyfXAKDgnP5e3m2Jkqvc1pSFISOBzO1w6H870ftKXuh6JtuPtKhFmovpIgsODqYXWv7/3PJmx2h47W6EtUF7NqzNAeHUlpH8urq/Yx/s/L2XLwtN4m1VNeDibX12UyOd/7iFEf4Q1RECoEuPtKpi6YGj7TsxmY1A5xfPiLCQBsPXSGa14oYOWuKCtI50IJuAdmk+DDuyfUvXcYqVralCkQG+usPBcb69foRSM+wjcX0o9kQQ9ZX0kjtPpM9f6uhqd1YuZQZyhlc8kpbnp1LVW1dn2M0ZHoGs7kA906xJHaIZZjZ6rZceQMg7q1N8bw3Tbkvxkx3bFxdYDcXFi0yAAV/iIIrWqGGaAWGQAv3uQcmJj+wCcAVNbYjfFbDSHKA2+EEIK3bx/PgJREHvi/LWTOW8KB8kq9zXKSnQ1z5/r9azHiI3zjUtYQsWXHdUOrUu5GKhF/pqq27vWmkuibKEIJeDNkpLRv0FFy5PR5Ha0JDno9wnuj8bwBOTnBn5sg2tFivgctjxsINnt9mPO2N4xXZ0lronpS45Y4W1XLXW9vYOWu4/TqHM+q301zrlCDaTRDfbTBR6vP1Ejf1d8/L+LpL3cDUPyXy/U1RiPUpMZ+0j7OyrPXj2L4nz4n1uJ6UDFK8K8FjPTD8hdV7iX4aPWZGum7cot3NKJCKC3QMcFKRkoiB0+e5911JcYK/jWDGqypiEbGeczcU15RraMloUcJeCv848cjqLY5+J/3vmPVkAnGCf41g8HbF4VCE969s/5R4Lf/u1lHS0KPCqG0wpDuHYizmqiqdfDgTjv5Bi5lF5SJbsI5BqOISjyzxE6dr21hy8hDCXgrCCH467XDueedjRSXV1IzdjIxBhW2NpdK1TvGrxoPRWs0c4/UOuqH0v/20kH62KUTSsB94MrhPbCYBHe9vYGHP97GH783hFhLywMG9Cof2qbOJT0nG9a78VAYHy/3SP/kRBbdNo6573/Hja+sAZxPzot/eTEmk2+Ts4QrSsB95LKs7vxwdC/eXnOA9ftP8sjVwxjj0XnipqSghM25m9n02iYcdoe2U60FGw0mG/bZqTbATPUKg9PCPTJ5YDLLfjOZBYt38M7aA2w/coZ+v/+UsemdOVtlo/DoWf7ygyx+PM6Ys4IFihJwP/jrtRcweVAyc9/fwrUvFDBxQFdev2UsR9Yeoji/mISkBJbcuwRbla1uhlDNp1oLJkGersYvp1rnmeoVYUAr90hCjIWFP8jirin9+WTLEUpOVFJ09CyFR50lZx/4vy0M7dGRrF4dQ2+7RqiBPAFw+NR5bnh5NcXllQzqGM/EBQU4auwIk8Bhd4A7JCfAEmcJHw88yCxc6ExptNudiTsLFtDyrO8qBq5ojQDukVq7g5+9uZ4vC+sna7n94r48eMUQbWzUAG8DeZSAB4iUkjty17NsxzEmLd5H/23lCJNAmARSSkwWEyNuHcHwnOFRKd6gwtoKY3GgvJL31pfUDfxZdNs4Jg9M1tkq31AjMYOMEIJZ3QexbMcxqi0W7AisVjOXPT2LyvJKNfchagJhhbHonZTAfZcOYtrgVH79n03c/NpaOiVYeeEno7mwX3jOsak88Dbw20fO8F7FSuQZK13Wm7h21CD+8Hx0i7ZC4Q0jRciqbXYGPbgEAKtZsOvRy/Q1qBW8eeBqJGYbuGZ6B84sz0J0qOXk1Go2m+P1Hb6ud5V9hcILRivzYDXVS9/qudN1tKRtKAFvA9nZ8MIjnevef/TveP1uTqP9QhQKDwxT5sHl5JjWrOaeaRkA/Orfm6isselkUNtQAt5G/m//1rrX1afi9Ls5DfMLiSLC4YnHIDa2WkM8FHY2cnLua3+Cmy7sw6rdx7nnnU3anVdDVCdmG9hx5Ayr954A4Ny6fpgx65fCrPKoQ0s4pNgYyMYWO7RDZWczTs599/yGN1fvZ9mOY6Q/8AnzvzeEm7PTw2YEp/LA28AfP3R63zdd2Id35w+om11Gl99I4ylujCYmkUY4PPEYzEavMwKGys5mHgM6t4thxf1T6zZ5+OPtvPb1Pm3OrwFKwNtAdr8khIA3V+/n9s8+Z3WHr1l5upBTlTU6GRTYnJmKADDSvGLeCAcbIXR2enFyeicl8PUD0/jBqJ4APJ+/R5vza4BKI2wjZ6pqWb//JN/uO8G3xSdYv/8k7eOszJnUj1suSqddrIpSRSxGyovzRjjYCIawc8OBk/zgn98AsPfPlxkqjKJGYoaIL7Yf447c+mvc+vBMEpWIKxSGZseRM8z+x0oAOsZb2Tz/Up0taogaiakhDofkeEU1FrOJfyzfWbf8ujFp9fNpKhQKw3LvvzcBkNQuhvXzLtHXGD9QAt5GqmrtXPXs1xQdO9tg+bL7JpGR0l4nqxQKhT9kpCZSdOws5edq+GpnWdjUSFHuYYDY7A4Wf3eYu97eUCfeA1ISWfiDLFb9bqoSb4UijPjHdSN48PLBANz82lr+d12Jzhb5hvLAA2DXsbP84l8b2Hmsgm4d4vj+yJ5MGtiVy7N6EKNCJgpFeFFQgCU/n5smTuYR96I95fxwjPHrGikB94Nqm52Fnxby728PkBhr4Z83jmLm0G6YDdRbrYgSDJC1ERF4DCKyxsRi/dW71Eron5Kot2U+oQTcD/aWneONb4oxCfj0/omkdIjT2yRFNGKgEZZhj8cgIlNNNX+IPcRDVT35fNtRMru1Z/rg1LpNa2wOSs9W0aNjvGFSDJWA+0Fmt/b06BjH6fO1Kr9bJ/SaLNpQqPlDg0dSEggBJhPExDB70lByN9Sy+eBpfrpoHX27tmPG4BQ6xFl5c/V+Ss9Wk9mtPa/fOpbuHeP1tl4JuD8IIai2OThXY+fI6SoydHjMimYBKykoIXd6LvYae3hNFh1sVN2b4FBQAPfeCw6HcxToU0+ROm0Cy6dKTpyr4d/flvBVURmLvtlPjd3BoNT23HxROs98uYsJf/mSfsmJ/HhsGrdclI7FrE/fV6sCLoSIA1YAsa7t35NSzhdCPA58D6gB9gC3SilPaWir7kgpKT9Xw+VZ3XUT72gWsOL8Yuw1dqRdhtdk0cFGTXUUHNxPMg6H0wsvLwecjlpSYiy/mJrBL6ZmIKWkssZOQowZIQTTB6fw6XdHWLPvBI98soP3Nxziigu6E2c1c77GxrkaO2YhyOrVkdF9OtM1MVazS/DFA68GpkkpK4QQVmCVEOIz4AtgrpTSJoR4DJgL/E4zSw2Ae9DqJ1uO8KyUCBHaOFi0C1j6lHTMMea6Bix9SrreJulHdrYS7rbi45OMEKJByDSzWwcyu3VASsnSbcdYsHg7jy8tqltvNQscEuwOiUnAby4dxM8n99ckbt6qgEvnWPsKt22u/1JK+bnHZquBa4NuncH4bOtRAN1SBaNdwNKy08hZnhO1ISRFkGnjk4wQglnDujFzaCo1dgfna+zEWc3EWc1U1drZeug0b3xTzONLi9h44CTP3jCKOKs5qJfgUy0UIYQZWA9kAM9JKX/XaP3HwH+klG81s+8cYA5A7969R+/fvz8YdoecyhobYx5ZRo3Nwab5l+pW3ySaY+CKyCYS720pJXfkrmfZjmMsvXcSg7oFNsCvTbVQpJR2YIQQohPwgRBimJRyq+vAfwBswNte9n0JeAmcxawCst4A/PSNdVTW2Bneq6OuxanSstMi5ub2G5X7HLFEYv+OlJKvdpZRePQMPTvFM0CDfjO/lEhKeUoIkQ/MArYKIW4GrgCmy1CWNQwhUkqW7yilYK+zg2PRbeN0tihKUbnP+tGWhtPHfSOpf8fukOQVlvLqqn0U7C2ne8c4nrtxlD4xcCFEMlDrEu94YAbwmBBiFs5Oy8lSysqgW2YASs9Wcevr37Lt8BkSYsy8dstYOiXE6G1WdKJyn/WhLQ2nH/tGSv/O2apabn5tLRsOnKJbhzgevHwwOdnpmvWb+eKBdwcWueLgJuBdKeViIcRunKmFX7iyMVZLKe/UxEqd+Nmb69l2+AwAH9w1IeD4lcIHWvPUVO6zPrSl4fRjX186qI0eQSs9W8XP39rAdwdP89g1WfxgVC+sGueH+5KF8h0wspnlGZpYZBBOn69lc8kpYi0mvn5gmqa5nOGEJh1NvnhqKvdZH9rScPq5b0v9O0aPoH21s4zfvLuJimobz1w/ktlZ3UNyXjUS0wur95bjkHBR/yQl3i4062jy1VNTuc+hpy0NZxAbXSNH0J78Yif/WL6LgamJ/OuOCxmYGrondSXgXhjftwsA3+wp19kS46BZR5MKjxibtjScQWp0jXaL7C2r4LuDp1n83RGW7TjGlEHJvPCT0UHP824NJeBeMJsEQkDfru30NsUwaNbRpMIj4YNOgWgj3SJFR89y+dMrsTkknROs3HfJQO6a0l+XeihKwL1w5HQVUkLh0bNU2+zEWkLbshoRTUdCGig8EokDSoKCzoFoI9wiX2w/xn3vbqJdrIWnfjyCSQOSdZ0PQAm4FwamtufuqRk8m7eb9cUnuSija1CPH64iEekDiSJxQEnQMHIgOgTsLq3gDx9swWwSfHDXBEM8nSsBb4GrR/bk2bzdrN5bHlQBVyJhXCJpQEnQMVogOgTsLj3LA+9v4dT5WvaWVdAh3sqLPxltCPEGJeAtUnz8HADVdkdwj6tEwrBEyoASTTBSIDpEFB+vZN3+kwDcPTWDnIv6kNLeODNxKQFvgRG9OzGqdyde/GovdrvkpxP7BmUWDiUSGtLGTjZV8bAVjBCIDiElJ52DzF/4yWhmDeumszVN8akaYbAYM2aMXLduXcjOFwyqbXb+9PF23l5zAIB1D84ISl54uMbAm8UoQ+SMPtojEjHKd68B+8vPMfWJfMamd+GdOy7UdR7MNlUjjGZiLWYe/X4WX+8+TnF5JT95ZQ3PXD+SAW1M1o+YzkAjiWaUd7KFHD2/+xA0HPvLK3FI+MmFfQwziXFj9JmZIAxZ+utJ3DN9AIVHz/LYkqLWd4gWmhNNvXB3spnNUdPJpitevvuSghJWLlxJSUGJNud1Nxzz5jn/FhRocppVu48jBPRLNkaHZXMoD9xHYi1mfjV9AE8v30VKBzW0vo5QZSb44nFFYSebrjTz3Ze89Bm5d6/FbgdzrEWbDKsQPGlJKfnvxkPMHNKNoT06BvXYwUQJuB+YBCS3j2Xx5sPcO2OAoXqjdSMUounPo3qUdbLpSuPvHij+xePYbRORmLBX27TJsAqB07C7tILSs9VMGZQc9GMHExVC8QMhBHdN6c+ZKhsfbjystznGITsb5s5tIpwFBbBwoQ9PuK1taKQwjaIhnt99fj7pjj2YsSOwYzajTYaVu+FYsECTuLuUksXfHQFgTHqXoB472CgP3E/e33AQgAwNpkcyMv5mzfjsNPuyYRQOIAlLpkwhLXYBOdVvUmzqT/qz92vXUa/hk9ZHmw/zj+W7GNe3C/0MMmDHG0rA/WDJ1iNsPXSGWy5KZ2pmit7mhIxARo76HKb0ZUMV2w4PXN9TWn4+aWH4PW09dJolW4/y8sq9ZHZrr3vqoC8oAfeDf+bvIbVDLL+/bLDepoSUQEaO+uw0+7qhim2HB2H6PX29+zg3vboGh4SZQ1N55OosXYtU+YoScD84dPI8gGbz2xmVQEaO+uw0K+9aoTMHT1Zy+6J19EtOZNFt4+jZqe2jrUOFEnAfWb//BOXnaujVOXy+3GAR6PByn52xMPXaFOHP9sNn+OU7G7BLyfM3jgor8QYl4D6zeu8JAP554yidLWlKKIblR8zIUYXCgzvfWs+BE5U89L0hbR5drQfRFQsIkNOVtTy9fBfpSQlk9TRWUr+7gzFvXh6503O1G/2mUEQgT/14BBaTYNXu8Jw6UQm4D5ysrKHa5mBQt/YIYayOjeY6GBUKhW/075pIp4QYwqC/slmUgPtAn6QErGbBxgOn9DalCe4ORmEW+pSm9Xm0jqIJ6rPTnSeX7eR4RTV3TumvtykBoWLgrWB3SFbuKsMhMeSwWl3rVxupEmG4oT47/9GgAmGsxYTZJBjVu3NQjhdqlIC3wmur9vHopzvoEGfhlov66m1Os+jWwajKtwaO+uz8Q6MGLz7GjN0hsTtkWOR9N0aFUFrBnfP9+A+HM6RHB52t0R6/SoGq8q2Boz47/9CgHo6Ukp3HzpIYayGUE9sEE+WBt8J1Y9N45svdPJe3m5lDjTelUjDxe8i8GoQTOOqz8w8N6uE8vrSIT7cc5ZfTMrCYw9OXVQLeClJCVa0dSxg+XvlLQJMtq0E4gdPWzy6CpzNrQjMNXlvGP/wzfzf/zN/DDeN7c98lAzUxORQoAW+F4xXVVFTbOHCiMmzjZL6iJlsOI6KxE9SjwQukwBo4nbG3Vu/nr0uKuGpEDx65apjhUoP9QQl4K6R1SeCi/kl8s6ecyY/n8eDlQww5O3UwUDOyhxFR3gnqz9Piyl1l/OWzQk5V1nL49HmkhOmZKTzxw+GGrzbYGkrAfeCFm0bz8ebDLPqmmJ+/vZ5XcsYwfXCq3mZpghoyHyZEeY10f54W1+w9wbbDZxjaowPXju7F0B4dmJaZErZxb09EKHtfx4wZI9etWxey8wWbimobVz27ij1l57jlonTmf29IWD9+RT3hHkMOd/vbiK8x8N2lFcz4+1fMnZ3JzyaH54AdIcR6KeWYxsuVB+4HibEWPrlnIg99tI03vilmamYKkwcab3CPwgciIYYc5R3IvjwtnqqsYdE3xQARORl5q88QQog4IcRaIcRmIcQ2IcTDruU/dL13CCGatAyRSpzVzH2XDEQIWPjpDk5X1uptkiIQ1DybEc3ba/Yz88kVjHlkGW+u3k9Odh+uHtFTb7OCji9BoGpgmpRyODACmCWEuBDYCvwAWKGdecYkpUMc/7xhFIVHz/LWmv16m6MIBDWQJmIpPVvFQx9t40RlDXMm9eOzX03kT2GebeKNVgVcOqlwvbW6/ksp5Q4pZZGm1hmY2VndGdy9A6+t2ket3aG3OQp/0Xhmc4V+xFnNpLSP43yNnRPnakiIMettkmb41A0rhDALITYBpcAXUso1mloVJozu04mTlTXYHeE5DDfsCHb1vuxsmDtXiXeE0SHOylu3j+fSoan8d9MhrnhmFYdPndfbLE3wScCllHYp5QigFzBOCDHM1xMIIeYIIdYJIdaVlZUFaKYxOXq6GoeEzHlLWLrtqN7mRDbuTsd585x/VQlWRQv07dqOv/9oBE/+aARnq2ys339Sb5M0wa9ESCnlKSAfmOXHPi9JKcdIKcckJ0dWxsZDVw5hWE9ngauCPeE5o0fYoDodFX5QcqKSv3xWyP+89x1d2sUwvm8XvU3SBF+yUJKFEJ1cr+OBGUChxnaFBb06J/DXa4ZjEpBbUMz2w2f0NilyUZ2OCj+Y/9E2XvhqDxcP6MoHd11ESoc4vU3SBF888O5AnhDiO+BbnDHwxUKI7wshDgLZwCdCiKVaGmpUhvTowOe/nkRK+ziuef4b3l6z35ilKcN99hfV6ajwkWqbnW+LT3D1iB48/5PR9Elqp7dJmtHqQB4p5XfAyGaWfwB8oIVR4UZGSns+unsC4/68nD98sJVhPToyPK2T3mbVo/WglVCNCPR34EqUj1Q0BDp8B8t3lHK2ysZVEZj33Rg1EjNIpHSI455pGTz95W7eW3/QWALuT+Ejf39wRh3RaFS7IgCfy7jq9B288XUxvbskcPGArpqfS2+UgAcRt2gfMlrKkq+FjwL5wRm1Kp5R7QpzPMu4Fg9NYue1Azl4rqZufacEK5/cM5GeneID+w7a6LGv3lvO2uIT/HrGQKwRUKyqNZSAB5E4q3PAQEZKos6WNMLX2V8C8dSTkoxZFS/Kq/VpgZSSZct2czA1npT9Z/lydjp4iDfAqcpaJvzlS64d3YvMXuNJyppOYuVZkmsquGDy5JY73drosR85fZ573tlIWpd45kzqF9A1hhtKwIPIhIyujOvbhbdW72fOpH50TfSxeE4o4oS+xI8D9dSfegrKy9tsf1tmWGmCmrIs6Lz2dTELzlXCdYOw1NoByOqSwMf/MxUAh0Py0ebDzPtwK++tP+jcaeY99Qf46CTD1q/kujFpTB+cSo9O8Q1P0IanJiklt7z2LaVnq1l67yTiI3j0pSeqnGyQ+WL7Me7IXUfXxFjmXTGYK4f3aLkGg9Fitb40JgsXOgfU2O3OtL4FC5wjGttAoDOsKELD1kOnueKZVXXvp1itJKd15OFbRpMQ09APlFJSa5es3FVGrMVMjMXEj15smv103Zg0UjvGsbnkFF/tLCOzg5kPF/6ImPOVCD9/C2Vnqxn76DIABqYmMu+KIUwcEDnjTlQ52RAxY3AKt1/cly92HONX/97El4WlPH7t8LrZ7ZtgtFhtMD11PwhoPk5FyPAU73fuuJDs/kletxVCEGMRDSY92fnIbPKLSumfksh3B0/x+//byn/WlTTYr/CMnUG/eKfu/YRtgusSDtM3qR0V1Tb2Hq+gvKKGU5W1DE/ryKxh3Yi1OD3t5PaxLL13Eqt2H+fNgmJuenUt//3FBEYYKZlAA5QHrhEOh+S5vN387YudxFhMxFlMxFrNxFpMrv9mYq0mYs5VELthHbE1VcRKO7HTpxLboxuxFhMx7u3c+zTY32Odtf51TDPLNZnHM8hhH+WBG5v0Bz4B4Ns/zCC5fdvrakspsTskNodk7b4T7D9RyYcbD7HOhyHvsRYT1TYHJgFd2sUihNMDb8ytE9KZ/72hbbbVCHjzwJWAa8ySrUfZeOAk1TYH1TY71bWO+tc21+uTp6k+U0FNfDuqrbEN1tfY2l7p0GISDcQ9PsYp8PExZuJc7+OsJuKsZud/i5n4GFPdulirmXirc5t49zbW+n3cy+JdDUyg8wwGNQauCBoOh6Tf7z8FYNejszXP7jhfY2f13nL6JCVQfq6GzSWnOHyqigGpiVwzqhcWkyB/ZymbDpyirMLdiSopOXGeseldEAKkhNlZ3RiY2l5TW0OFEvAwxeGQ1NjrRb/GLfq1DRuBGi8NROPlVbV2qtx/Xf/P19qpqnVwvsZOtc31utYecJXFWIunsJsaCH4DsW+uYYgxE2dp1DDE1Dc8DY5rMYf9pLThwJHT58le+CUAxX+5XGdrohMVAw9TTCZBnMnsSlG0hvTctXaHS9ztVNU4qLLZOV/TUPSrGyzz0jC43lfXOjheUdNgnft1oI2FOzzlKe7OhqFhI+DZkHg2GrGNniC8NThxVo1CUWFA54SYutdHT1fRrWNk1hUJR5SAK7xiNZuwmk10iNO+4ai114t5tYewezYAdQ1DjfMpwvnXub37tXtdlauYf/1yB9W1zte19gAbC7OpPmzkCj812zDEmOufGNzhKFdoyv2E0eRpwqOhiDdYYxFnNXPHxL68vHIfFy5czu5HZ0fEjO6RgAqhKKIOm91R3wDUNQz1DYXnE8P5Rk8TVY22cT91VHtpcAJtLKxmQZzVTEKMmYQYi8drp8AnxJiJj7HUvY+P8VgfYyHBtU2ce7nVUrdNvDWw0NMFDy3lTJUNUKGUUKNCKAqFC4vZRKLZRGKs9re/u7GoqvXSWHg8MdSHqOrDVedr7FTW2jlfY6Oyxk5FtY2ys9VU1tiprHEtr7UTqB82a2g3XrhpdNMVzWQZbZ5/KX3nOjszK2tsTfK/FaFHfQMKhYaEorGQUlLteqLwFPvKGudTwXlPsa+xU36uhpdW7AVgaI8OTQ/oZXCZZyMhME6IJ5pRAq5QhDlCiLr4eWcfti+vqCa3oBgpISc7vekGXgaXvbpqX90m0TJU3eiongiFIspISozluRtGYXNILnt6JTuONJpJysvsR5cMcY6svGpEj9AarPCKEnCFIgqZPjiV9+7M5tCp8zy9fFfDlV5mP/r3t86h798dPB1qcxVeUCEUhSJKSXdNNdasIDeqiVNVa+eFr/YA8MatY0Nin6J1lAeuUEQpO4+dBaClYplufvLKGgBG9e4U0XNMhhtKwBWKKMXqqpDpSwqiu8jUwh9coKVJCj9RAq5QRCmjejtzVg6dOk/W/KWcr7Gzv/wci787zHN5u/lo82HOVduotdcXVOuUENpyDoqWUTFwhSKK2TjvEkYu+IKz1TYG/3FJq9snqPRBQ6EEXKGIYjq3i2Hfwsv4Yvsx3l13kAv7daFHp3iqbXYOnjiPxWxiy6FT9O3ajhvG96F9COriKHxHCbhCEeUIIbh0aDcuHdpNb1MUfqJi4AqFQhGmKAFXKBSKMEUJuEKhUIQpSsAVCoUiTFECrlAoFGGKEnCFQqEIU5SAKxQKRZiiBFyhUCjClJBOaiyEKAP2h+yEoaErcFxvI3Qimq8dovv61bWHlj5SyuTGC0Mq4JGIEGJdc7NFRwPRfO0Q3devrt0Y165CKAqFQhGmKAFXKBSKMEUJeNt5SW8DdCSarx2i+/rVtRsAFQNXKBSKMEV54AqFQhGmKAFXKBSKMEUJeIAIIUYIIVYLITYJIdYJIca5lqcLIc67lm8SQrygt63Bxtu1e6zvLYSoEEL8Vi8btaKF732cx3e+WQjxfb1tDTYtXPslQoj1Qogtrr/T9LY12LRw7UlCiDzX/f5syA2TUqr/AfwHPgdmu15fBuS7XqcDW/W2T49r91j/PvC/wG/1tjWE33sCYHG97g6Uut9Hyv8Wrn0k0MP1ehhwSG9bQ3jt7YCLgTuBZ0Ntl5pSLXAk0MH1uiNwWEdbQo3XaxdCXA3sBc6F3qyQ0Oy1SykrPbaJc20XaXi79o0e22wD4oQQsVLK6hDbpyXerv0csEoIkaGHUSoLJUCEEIOBpYDAGYq6SEq5XwiRjvMm3gmcAR6UUq7UzVANaOHa2wHLgEuA3wIVUson9LM0+Hi7dte68cBrQB/gJinlB7oZqgEtXbvHNtcCd0opZ+hgoma0du1CiFuAMVLKu0Npl/LAW0AIsQxobqbXPwDTgV9LKd8XQvwIeBWYARwBekspy4UQo4H/CiGGSinPhMzwIBDgtT8MPCmlrBBChM7YIBPgtSOlXAMMdf3YFwkhPpNSVoXK7mAQ6LW79h0KPAZcGgpbg01brl0vlAceIEKI00AnKaUUTrU6LaXs0Mx2+ThjwetCbaNWeLt2IcRKIM21WSfAAfxRShn6zh2N8ON7zwPuj4bv3bWuF/AlcKuU8ms97dSC1r53vTxwlYUSOIeBya7X04BdAEKIZCGE2fW6HzAAZ0w4kmj22qWUE6WU6VLKdOAp4M+RJN4uvH3vfYUQFtfrPsAgoFgPAzXE27V3Aj4B5kaieLto9tr1RoVQAucO4B+uH20VMMe1fBLwJyGEDbDjjAee0MlGrfB27dGAt2u/GHhACFGL88njLillpJVb9XbtdwMZwDwhxDzXskullKU62KgVXu95IUQxzg7OGFcn/qVSyu2hMEqFUBQKhSJMUSEUhUKhCFOUgCsUCkWYogRcoVAowhQl4AqFQhGmKAFXKBSKMEUJuEKhUIQpSsAVCoUiTPl/W1eqoGOj8e4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 14.793638927416024 14.6796701068295\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  10711907 4936331 0.498716661561674\n",
      "compare to Census 10711908 Leip has Trump + Biden = 4935487.0 0.49880670337091354\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 10711908\n",
    "atlasTrump = 2461854.\n",
    "atlasBiden = 2473633.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 10711907 10711907.0\n",
      "state Trumpers before, after slice opn are 2461829 2461829.0\n",
      "state Bideners before, after slice opn are 2474502 2474502.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 144 grids for 14 districts\n",
      "starting grid no 0 at x = -85.41725741666669, y = 30.54932141666667 \n",
      "starting grid no 5 at x = -85.41725741666669, y = 32.484965583333334 \n",
      "starting grid no 10 at x = -85.41725741666669, y = 34.42060975 \n",
      "starting grid no 15 at x = -85.04144224999999, y = 31.71070791666666 \n",
      "starting grid no 20 at x = -85.04144224999999, y = 33.64635208333333 \n",
      "starting grid no 25 at x = -84.66562708333335, y = 30.93645025 \n",
      "starting grid no 30 at x = -84.66562708333332, y = 32.87209441666666 \n",
      "starting grid no 35 at x = -84.66562708333332, y = 34.80773858333333 \n",
      "starting grid no 40 at x = -84.28981191666668, y = 32.09783675 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Input geom 1 is INVALID: Self-intersection at or near point -84.281015999999994 33.772047000000001 (-84.281015999999993937 33.772047000000000594)\n",
      "<A>\n",
      "POLYGON ((-84.2964699999999993 33.7779150000000001, -84.2948239999999913 33.7777940000000001, -84.2948149999999998 33.7784959999999970, -84.2948090000000008 33.7789179999999973, -84.2947789999999912 33.7798889999999972, -84.2948010000000068 33.7804730000000006, -84.2948319999999995 33.7807779999999980, -84.2948899999999952 33.7811759999999950, -84.2949139999999915 33.7813969999999983, -84.2949059999999974 33.7816419999999979, -84.2948769999999996 33.7818830000000005, -84.2948639999999898 33.7820040000000006, -84.2948149999999998 33.7822729999999964, -84.2947929999999985 33.7823390000000003, -84.2946910000000003 33.7826680000000010, -84.2944430000000011 33.7831610000000069, -84.2942369999999954 33.7834810000000019, -84.2937859999999972 33.7839949999999973, -84.2932619999999986 33.7845200000000006, -84.2927239999999927 33.7840369999999979, TopologyException: Input geom 1 is invalid: Self-intersection at -84.281015999999994 33.772047000000001\n",
      "-84.2921620000000047 33.7838139999999996, -84.2917190000000005 33.7835290000000015, -84.2916949999999900 33.7835150000000013, -84.2910399999999953 33.7831240000000008, -84.2907000000000011 33.7828949999999963, -84.2897789999999958 33.7825179999999961, -84.2894079999999946 33.7825699999999998, -84.2891539999999964 33.7826299999999975, -84.2887580000000014 33.7826759999999950, -84.2887699999999995 33.7824240000000060, -84.2890639999999962 33.7812110000000061, -84.2891499999999922 33.7800719999999970, -84.2891699999999986 33.7797599999999960, -84.2891489999999948 33.7794629999999998, -84.2891159999999928 33.7792260000000013, -84.2889959999999974 33.7788679999999957, -84.2888129999999904 33.7785599999999988, -84.2885519999999957 33.7781909999999996, -84.2882580000000132 33.7778719999999950, -84.2879259999999988 33.7774919999999952, -84.2878019999999992 33.7772700000000015, -84.2877649999999932 33.7771339999999967, -84.2877610000000033 33.7769649999999970, -84.2877960000000002 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-84.2960789999999918 33.7755859999999970, -84.2965540000000004 33.7755839999999949, -84.2964699999999993 33.7779150000000001), (-84.2810159999999939 33.7720470000000006, -84.2810167225546394 33.7719841377455978, -84.2810138661313601 33.7721913236591789, -84.2810149403652673 33.7721904745410910, -84.2810159999999939 33.7720470000000006))\n",
      "</A>\n"
     ]
    },
    {
     "ename": "TopologicalError",
     "evalue": "The operation 'GEOSIntersection_r' could not be performed. Likely cause is invalidity of the geometry <shapely.geometry.polygon.Polygon object at 0x7fbf502c3c70>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTopologicalError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_185/1622649184.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     70\u001b[0m             \u001b[0;32melse\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;31m#precinct is a multiPolygon, do the polygon intersections individually\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mgeom\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvtdGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeoms\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m                     \u001b[0mintersxnArea\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mgridGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnG\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m             \u001b[0mintersxnArea\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgridGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnG\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvtdGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mintersxnArea\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mvtdPop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;31m# this precinct is at least partially in this grid and recorded votes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/geometry/base.py\u001b[0m in \u001b[0;36mintersection\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m         \u001b[0;34m\"\"\"Returns the intersection of the geometries\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 689\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgeom_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimpl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'intersection'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msymmetric_difference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/topology.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, this, other, *args)\u001b[0m\n\u001b[1;32m     70\u001b[0m             err = TopologicalError(\n\u001b[1;32m     71\u001b[0m                     \"This operation could not be performed. Reason: unknown\")\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_topology\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/topology.py\u001b[0m in \u001b[0;36m_check_topology\u001b[0;34m(self, err, *geoms)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mgeom\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgeoms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_valid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m                 raise TopologicalError(\n\u001b[0m\u001b[1;32m     38\u001b[0m                     \u001b[0;34m\"The operation '%s' could not be performed. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m                     \"Likely cause is invalidity of the geometry %s\" % (\n",
      "\u001b[0;31mTopologicalError\u001b[0m: The operation 'GEOSIntersection_r' could not be performed. Likely cause is invalidity of the geometry <shapely.geometry.polygon.Polygon object at 0x7fbf502c3c70>"
     ]
    }
   ],
   "source": [
    "#FAIL here with a bad precinct when building grid map in Georgia.  See below two blocks for fix and re-run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c3eb2c10-5caa-42ce-a70f-d87d6e275e22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034 1\n",
      "0 0.00016990724566590647\n",
      "1 2.9354445031118904e-11\n",
      "2 4.989851763673341e-17\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WTF is going on with this precinct?\n",
    "print(p, notPolyVTD[p])\n",
    "counter = 0\n",
    "for geom in vtdGeom[p].geoms:\n",
    "    x,y = geom.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    print(counter,geom.area)\n",
    "    counter+=1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2532d156-de55-4a64-ac38-1a373a9ab910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OK, kill the bad sub-polygons\n",
    "counter = 0\n",
    "for geom in vtdGeom[p].geoms:\n",
    "    if counter == 0:\n",
    "        goodGeom = geom\n",
    "    counter+=1\n",
    "vtdGeom[p] = goodGeom\n",
    "notPolyVTD[p] = 0\n",
    "x,y = vtdGeom[p].exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 144 grids for 14 districts\n",
      "starting grid no 0 at x = -85.41725741666669, y = 30.54932141666667 \n",
      "starting grid no 5 at x = -85.41725741666669, y = 32.484965583333334 \n",
      "starting grid no 10 at x = -85.41725741666669, y = 34.42060975 \n",
      "starting grid no 15 at x = -85.04144224999999, y = 31.71070791666666 \n",
      "starting grid no 20 at x = -85.04144224999999, y = 33.64635208333333 \n",
      "starting grid no 25 at x = -84.66562708333335, y = 30.93645025 \n",
      "starting grid no 30 at x = -84.66562708333332, y = 32.87209441666666 \n",
      "starting grid no 35 at x = -84.66562708333332, y = 34.80773858333333 \n",
      "starting grid no 40 at x = -84.28981191666668, y = 32.09783675 \n",
      "starting grid no 45 at x = -84.28981191666668, y = 34.03348091666667 \n",
      "starting grid no 50 at x = -83.91399675000001, y = 31.32357908333333 \n",
      "starting grid no 55 at x = -83.91399675, y = 33.259223250000005 \n",
      "starting grid no 60 at x = -83.53818158333334, y = 30.54932141666667 \n",
      "starting grid no 65 at x = -83.53818158333334, y = 32.484965583333334 \n",
      "starting grid no 70 at x = -83.53818158333334, y = 34.42060975 \n",
      "starting grid no 75 at x = -83.16236641666666, y = 31.71070791666666 \n",
      "starting grid no 80 at x = -83.16236641666666, y = 33.64635208333333 \n",
      "starting grid no 85 at x = -82.78655125, y = 30.936450250000004 \n",
      "starting grid no 90 at x = -82.78655125000002, y = 32.87209441666666 \n",
      "starting grid no 95 at x = -82.78655125000002, y = 34.80773858333333 \n",
      "starting grid no 100 at x = -82.41073608333332, y = 32.09783675 \n",
      "starting grid no 105 at x = -82.41073608333332, y = 34.03348091666667 \n",
      "starting grid no 110 at x = -82.03492091666666, y = 31.323579083333332 \n",
      "starting grid no 115 at x = -82.03492091666668, y = 33.25922325 \n",
      "starting grid no 120 at x = -81.65910575000001, y = 30.54932141666667 \n",
      "starting grid no 125 at x = -81.65910575000001, y = 32.484965583333334 \n",
      "starting grid no 130 at x = -81.65910575000001, y = 34.42060975 \n",
      "starting grid no 135 at x = -81.28329058333333, y = 31.71070791666666 \n",
      "starting grid no 140 at x = -81.28329058333333, y = 33.64635208333333 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 603499.8149445399 9356140.563914228 122.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 14   #GA congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f5a471b-867e-4db1-abdc-55f4b3d1f27e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034 0.00016990724566590647\n"
     ]
    }
   ],
   "source": [
    "print(p,vtdGeom[p].area)  #this will spit out the precinct number where we choked (again)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b538ed92-bf09-41ce-9758-8a764cbff950",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sigh, this precinct's geom is still whacked.  Convex-hull it\n",
    "# vtdGeom[2034] = vtdGeom[2034].convex_hull  #after this line, go back up and redo the grid code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5461515915376092e-06 8.813567499988873e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 0\n",
      "we have 2 non-opposing shorted wedges for tract no 1\n",
      "I am working on tract number 20 of 2796 tracts\n",
      "I am working on tract number 40 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 56 3 424616.1133028639 2.3632\n",
      "7 yoyos for tract,wedge,wedgePop,r= 57 3 402332.0206056189 2.1575\n",
      "7 yoyos for tract,wedge,wedgePop,r= 58 3 453660.82908572647 1.9808\n",
      "8 yoyos for tract,wedge,wedgePop,r= 58 3 38437.36930355325 0.9904\n",
      "9 yoyos for tract,wedge,wedgePop,r= 58 3 454909.0361485762 2.0083\n",
      "9 yoyos for tract,wedge,wedgePop,r= 58 3 289021.469486699 1.4994\n",
      "10 yoyos for tract,wedge,wedgePop,r= 58 3 74935.80594690554 1.2449\n",
      "10 yoyos for tract,wedge,wedgePop,r= 58 3 150570.3113783514 1.3721\n",
      "10 yoyos for tract,wedge,wedgePop,r= 58 3 214173.35408594037 1.4357\n",
      "11 yoyos for tract,wedge,wedgePop,r= 58 3 247437.28627688298 1.4675\n",
      "11 yoyos for tract,wedge,wedgePop,r= 58 3 230592.7413946971 1.4516\n",
      "I am working on tract number 60 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 63\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 76 3.0 2 90.0 0.7916 334280.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 76 7.0 0 84.8 0.8213 311187.4\n",
      "I am working on tract number 80 of 2796 tracts\n",
      "I am working on tract number 100 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 106\n",
      "I am working on tract number 120 of 2796 tracts\n",
      "I am working on tract number 140 of 2796 tracts\n",
      "I am working on tract number 160 of 2796 tracts\n",
      "I am working on tract number 180 of 2796 tracts\n",
      "I am working on tract number 200 of 2796 tracts\n",
      "I am working on tract number 220 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 229\n",
      "we have 2 non-opposing shorted wedges for tract no 234\n",
      "I am working on tract number 240 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 242 1 384917.2486471181 1.795\n",
      "8 yoyos for tract,wedge,wedgePop,r= 242 1 48870.963030407846 0.8975\n",
      "9 yoyos for tract,wedge,wedgePop,r= 242 1 488737.9778742684 2.2583\n",
      "10 yoyos for tract,wedge,wedgePop,r= 242 1 107464.15910989675 1.5779\n",
      "11 yoyos for tract,wedge,wedgePop,r= 242 1 461426.19417125545 1.9181\n",
      "11 yoyos for tract,wedge,wedgePop,r= 242 1 318318.0681338988 1.748\n",
      "12 yoyos for tract,wedge,wedgePop,r= 242 1 204011.2622715834 1.6629\n",
      "12 yoyos for tract,wedge,wedgePop,r= 242 1 252006.63739216313 1.7055\n",
      "12 yoyos for tract,wedge,wedgePop,r= 242 1 283537.48267198674 1.7267\n",
      "13 yoyos for tract,wedge,wedgePop,r= 242 1 301799.5403577011 1.7374\n",
      "we have 2 non-opposing shorted wedges for tract no 245\n",
      "7 yoyos for tract,wedge,wedgePop,r= 258 3 365349.9194263181 1.62\n",
      "8 yoyos for tract,wedge,wedgePop,r= 258 3 25163.178572808567 0.81\n",
      "9 yoyos for tract,wedge,wedgePop,r= 258 3 365518.24630168756 1.6242\n",
      "9 yoyos for tract,wedge,wedgePop,r= 258 3 240232.96594136258 1.2171\n",
      "10 yoyos for tract,wedge,wedgePop,r= 258 3 54213.52078566502 1.0136\n",
      "10 yoyos for tract,wedge,wedgePop,r= 258 3 100076.88447693063 1.1153\n",
      "10 yoyos for tract,wedge,wedgePop,r= 258 3 166106.25187006325 1.1662\n",
      "11 yoyos for tract,wedge,wedgePop,r= 258 3 204572.08942610983 1.1916\n",
      "12 yoyos for tract,wedge,wedgePop,r= 258 3 185357.1592942739 1.1789\n",
      "I am working on tract number 260 of 2796 tracts\n",
      "I am working on tract number 280 of 2796 tracts\n",
      "I am working on tract number 300 of 2796 tracts\n",
      "I am working on tract number 320 of 2796 tracts\n",
      "I am working on tract number 340 of 2796 tracts\n",
      "I am working on tract number 360 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 376\n",
      "I am working on tract number 380 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 380\n",
      "we have 2 non-opposing shorted wedges for tract no 396\n",
      "I am working on tract number 400 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 409 1 394248.49689289846 2.101\n",
      "we have 2 non-opposing shorted wedges for tract no 414\n",
      "we have 2 non-opposing shorted wedges for tract no 417\n",
      "I am working on tract number 420 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 420\n",
      "7 yoyos for tract,wedge,wedgePop,r= 423 1 266970.81148332864 1.1632\n",
      "8 yoyos for tract,wedge,wedgePop,r= 423 1 6682.600062639016 0.5816\n",
      "9 yoyos for tract,wedge,wedgePop,r= 423 1 289688.7877193823 1.2138\n",
      "10 yoyos for tract,wedge,wedgePop,r= 423 1 21486.41022642795 0.8977\n",
      "10 yoyos for tract,wedge,wedgePop,r= 423 1 149646.76331551385 1.0557\n",
      "11 yoyos for tract,wedge,wedgePop,r= 423 1 247341.2763823006 1.1347\n",
      "12 yoyos for tract,wedge,wedgePop,r= 423 1 200958.0927607405 1.0952\n",
      "7 yoyos for tract,wedge,wedgePop,r= 429 0 419027.2231482314 2.6044\n",
      "8 yoyos for tract,wedge,wedgePop,r= 429 0 70632.9505620405 1.3022\n",
      "9 yoyos for tract,wedge,wedgePop,r= 429 0 422098.0433742067 2.8602\n",
      "10 yoyos for tract,wedge,wedgePop,r= 429 0 203366.3139552084 2.0812\n",
      "11 yoyos for tract,wedge,wedgePop,r= 429 0 410979.1278273186 2.4707\n",
      "11 yoyos for tract,wedge,wedgePop,r= 429 0 338816.52995934687 2.276\n",
      "11 yoyos for tract,wedge,wedgePop,r= 429 0 269246.5836533793 2.1786\n",
      "12 yoyos for tract,wedge,wedgePop,r= 429 0 236149.10838340354 2.1299\n",
      "7 yoyos for tract,wedge,wedgePop,r= 431 3 618118.7604011818 2.6676\n",
      "8 yoyos for tract,wedge,wedgePop,r= 431 3 104175.52867263067 1.3338\n",
      "9 yoyos for tract,wedge,wedgePop,r= 431 3 638014.3715243348 2.7983\n",
      "9 yoyos for tract,wedge,wedgePop,r= 431 3 514858.05141184083 2.0661\n",
      "10 yoyos for tract,wedge,wedgePop,r= 431 3 150815.15581603517 1.6999\n",
      "10 yoyos for tract,wedge,wedgePop,r= 431 3 301618.69134864875 1.883\n",
      "11 yoyos for tract,wedge,wedgePop,r= 431 3 414284.5895356138 1.9745\n",
      "11 yoyos for tract,wedge,wedgePop,r= 431 3 353280.10720890865 1.9288\n",
      "11 yoyos for tract,wedge,wedgePop,r= 431 3 327766.59287154063 1.9059\n",
      "11 yoyos for tract,wedge,wedgePop,r= 431 3 314935.42376234854 1.8944\n",
      "I am working on tract number 440 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 442\n",
      "I am working on tract number 460 of 2796 tracts\n",
      "I am working on tract number 480 of 2796 tracts\n",
      "I am working on tract number 500 of 2796 tracts\n",
      "I am working on tract number 520 of 2796 tracts\n",
      "I am working on tract number 540 of 2796 tracts\n",
      "I am working on tract number 560 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 572\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 573 7.0 1 52.0 2.3595 233691.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 573 8.0 1 52.0 2.3507 233691.7\n",
      "I am working on tract number 580 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 583\n",
      "I am working on tract number 600 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 619\n",
      "I am working on tract number 620 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 620\n",
      "we have 2 non-opposing shorted wedges for tract no 630\n",
      "I am working on tract number 640 of 2796 tracts\n",
      "I am working on tract number 660 of 2796 tracts\n",
      "I am working on tract number 680 of 2796 tracts\n",
      "I am working on tract number 700 of 2796 tracts\n",
      "I am working on tract number 720 of 2796 tracts\n",
      "I am working on tract number 740 of 2796 tracts\n",
      "I am working on tract number 760 of 2796 tracts\n",
      "I am working on tract number 780 of 2796 tracts\n",
      "I am working on tract number 800 of 2796 tracts\n",
      "I am working on tract number 820 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 826 6.0 3 45.2 2.1663 367623.3\n",
      "I am working on tract number 840 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 842 1 379395.02885797806 1.6982\n",
      "I am working on tract number 860 of 2796 tracts\n",
      "I am working on tract number 880 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 889\n",
      "I am working on tract number 900 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 909\n",
      "I am working on tract number 920 of 2796 tracts\n",
      "I am working on tract number 940 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 942 2.0 2 90.0 0.8478 264509.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 958 3 382760.8081274048 2.4072\n",
      "we have 2 non-opposing shorted wedges for tract no 959\n",
      "I am working on tract number 960 of 2796 tracts\n",
      "I am working on tract number 980 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 984\n",
      "we have 2 non-opposing shorted wedges for tract no 986\n",
      "I am working on tract number 1000 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1002 1 326973.90004457126 1.5046\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1002 1 30599.785967423755 0.7523\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1002 1 335657.93139272276 1.6326\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1002 1 100998.22390138108 1.1925\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1002 1 260084.23119291043 1.4126\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1002 1 329924.87764546275 1.5226\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1002 1 317041.596683857 1.4676\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1002 1 295764.2360652052 1.4401\n",
      "we have 2 non-opposing shorted wedges for tract no 1017\n",
      "we have 2 non-opposing shorted wedges for tract no 1019\n",
      "I am working on tract number 1020 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1033 5.0 0 85.5 0.9684 206537.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1033 6.0 0 85.5 0.9137 206537.4\n",
      "I am working on tract number 1040 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1052 4.0 0 115.2 0.9396 242080.0\n",
      "I am working on tract number 1060 of 2796 tracts\n",
      "I am working on tract number 1080 of 2796 tracts\n",
      "I am working on tract number 1100 of 2796 tracts\n",
      "I am working on tract number 1120 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1137 3.0 1 83.9 0.7056 193778.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1137 4.0 1 83.9 0.6987 193778.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1137 5.0 1 83.9 0.692 193778.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1137 6.0 1 83.9 0.6852 193778.4\n",
      "I am working on tract number 1140 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1148\n",
      "I am working on tract number 1160 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1173\n",
      "we have 2 non-opposing shorted wedges for tract no 1174\n",
      "I am working on tract number 1180 of 2796 tracts\n",
      "I am working on tract number 1200 of 2796 tracts\n",
      "I am working on tract number 1220 of 2796 tracts\n",
      "I am working on tract number 1240 of 2796 tracts\n",
      "I am working on tract number 1260 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1267 3 380831.5200162392 1.565\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1267 3 27556.7641287889 0.7825\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1267 3 380845.47820385866 1.5654\n",
      "I am working on tract number 1280 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1290 2.0 2 90.0 0.8044 366144.7\n",
      "I am working on tract number 1300 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 7.0 0 110.9 1.1671 196515.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 8.0 0 110.9 1.1436 196515.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 9.0 0 110.9 1.1206 196515.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 10.0 0 110.9 1.0981 196515.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 11.0 0 110.9 1.076 196515.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 12.0 0 110.9 1.0544 196515.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 13.0 0 110.9 1.0332 196515.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 14.0 0 110.9 1.0125 196515.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1313 1 474076.0972876261 1.7288\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1313 1 39590.86737267458 0.8644\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1313 1 474078.7027452211 1.7288\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1313 1 261842.7772967457 1.2966\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1313 1 69235.88024216567 1.0805\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1313 1 131318.53177096718 1.1886\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1313 1 200116.51693446754 1.2426\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1313 1 167327.53162723593 1.2156\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1313 1 184214.87436897034 1.2291\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1315 8.0 3 90.0 1.6129 371684.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1315 1 28940.335085686354 0.6789\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1315 1 600975.1456693637 2.3946\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1315 1 459762.881347561 1.5368\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1315 1 83829.47253031691 1.1078\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1315 1 351474.7837774568 1.3223\n",
      "loop31.0, tr1315,wedgePops777985.94, 184768.3, 206560.3, 193306.9, 193350.5, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01000 \n",
      "   targetWP, latest drx4 are tWP,dr, 193456.0,0.0524, 193456.0,-0.1072, 193456.0,0.0037, 193456.0,0.0003\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1315 1 206560.33082647415 1.2151\n",
      "loop32.0, tr1315,wedgePops706998.6, 184768.3, 135573.0, 193306.9, 193350.5, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01000 \n",
      "   targetWP, latest drx4 are tWP,dr, 193456.0,0.0524, 193456.0,-0.0536, 193456.0,0.0037, 193456.0,0.0003\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1315 1 135572.9925523395 1.1614\n",
      "loop33.0, tr1315,wedgePops746429.41, 184768.3, 175003.8, 193306.9, 193350.5, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01100 \n",
      "   targetWP, latest drx4 are tWP,dr, 193456.0,0.0524, 193456.0,0.0268, 193456.0,0.0037, 193456.0,0.0003\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1315 1 175003.801409412 1.1882\n",
      "loop34.0, tr1315,wedgePops763476.98, 184768.3, 192051.4, 193306.9, 193350.5, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01100 \n",
      "   targetWP, latest drx4 are tWP,dr, 193456.0,0.0524, 193456.0,0.0134, 193456.0,0.0037, 193456.0,0.0003\n",
      "I am working on tract number 1320 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 7.0 1 49.6 2.501 267655.6\n",
      "I am working on tract number 1340 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 4.0 3 90.0 1.523 198307.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 5.0 3 90.0 1.4822 198307.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 5.0 0 112.6 0.8264 199257.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 6.0 0 112.6 0.8014 199257.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 7.0 0 112.6 0.7771 199257.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 8.0 0 112.6 0.7535 199257.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 9.0 0 112.6 0.7307 199257.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 10.0 0 112.6 0.7085 199257.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 11.0 0 112.6 0.6871 199257.2\n",
      "I am working on tract number 1360 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1371\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1372 3 369094.4867941217 1.2115\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1372 3 251163.71801404952 0.6057\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1372 3 384376.34576835483 1.2599\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1372 3 300928.862891395 0.9328\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1372 3 314404.39979935106 1.0963\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1372 3 349846.4722644537 1.1781\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1372 3 372816.73588761216 1.219\n",
      "we have 2 non-opposing shorted wedges for tract no 1373\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1377 1 461301.87772913015 2.4949\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1377 1 52501.30256311153 1.2474\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1377 1 465473.28007104463 2.5586\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1377 1 438404.26379038725 1.903\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1377 1 133513.93672589335 1.5752\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1377 1 358929.2245079281 1.7391\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1377 1 234170.22483445233 1.6572\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1377 1 171927.46455121948 1.6162\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1377 1 199776.57359125582 1.6367\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1377 1 217162.75460381564 1.6469\n",
      "I am working on tract number 1380 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1381\n",
      "we have 2 non-opposing shorted wedges for tract no 1382\n",
      "we have 2 non-opposing shorted wedges for tract no 1396\n",
      "I am working on tract number 1400 of 2796 tracts\n",
      "I am working on tract number 1420 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1422\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1428 6.0 3 45.3 2.1295 395113.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1429 1 371209.00760104985 1.573\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1429 1 101131.34865074564 0.7865\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1429 1 371339.21763486776 1.5752\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1429 1 159042.62627966717 1.1808\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1429 1 356417.77886023314 1.378\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1429 1 277335.2062605476 1.2794\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1429 1 210289.17298531072 1.2301\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1432 3 285273.60128522 1.6812\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1439 1 327862.05764630984 1.3961\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1439 1 63044.29285939387 0.6981\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1439 1 327866.11174117774 1.3963\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1439 1 96625.1543032502 1.0472\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1439 1 198927.21467043698 1.2217\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1439 1 305284.26738873054 1.309\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1439 1 259154.33294407002 1.2654\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1439 1 228367.66097987027 1.2435\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1439 1 241728.36750972364 1.2545\n",
      "I am working on tract number 1440 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1440\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1453 1 325735.7559995561 1.5822\n",
      "I am working on tract number 1460 of 2796 tracts\n",
      "I am working on tract number 1480 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1492\n",
      "we have 2 non-opposing shorted wedges for tract no 1495\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1497 9.0 1 50.6 2.5624 258140.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1497 10.0 1 50.6 2.4032 258140.4\n",
      "I am working on tract number 1500 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1519 1 585339.128709185 2.5474\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1519 1 155146.01964854362 1.2737\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1519 1 591670.9154131716 2.677\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1519 1 542196.4776147844 1.9754\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1519 1 186705.1510410742 1.6245\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1519 1 344137.63538161456 1.8\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1519 1 477368.2186417891 1.8877\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1519 1 414103.9207062155 1.8438\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1519 1 377954.3864368299 1.8219\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1519 1 358825.60550429276 1.8109\n",
      "I am working on tract number 1520 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1520\n",
      "I am working on tract number 1540 of 2796 tracts\n",
      "I am working on tract number 1560 of 2796 tracts\n",
      "I am working on tract number 1580 of 2796 tracts\n",
      "I am working on tract number 1600 of 2796 tracts\n",
      "I am working on tract number 1620 of 2796 tracts\n",
      "I am working on tract number 1640 of 2796 tracts\n",
      "I am working on tract number 1660 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1664 0 309438.09915777977 1.4964\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1664 0 55994.40994285839 0.7482\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1664 0 319863.2966383422 1.6324\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1664 0 169848.22995275326 1.1903\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1664 0 301035.66389936476 1.4113\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1664 0 279548.86346697604 1.3008\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1664 0 227795.91234792312 1.2455\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1664 0 196695.86023465986 1.2179\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1664 0 183189.1733267347 1.2041\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1666 0 241134.30860310546 1.3223\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1666 0 41774.50275127523 0.6611\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1666 0 249203.82716026434 1.3843\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1666 0 67288.81871876924 1.0227\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1666 0 151484.76949474966 1.2035\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1666 0 234014.8031340026 1.2939\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1666 0 198971.69569493423 1.2487\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1666 0 175472.55196277157 1.2261\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1666 0 187756.14818251925 1.2374\n",
      "I am working on tract number 1680 of 2796 tracts\n",
      "I am working on tract number 1700 of 2796 tracts\n",
      "I am working on tract number 1720 of 2796 tracts\n",
      "I am working on tract number 1740 of 2796 tracts\n",
      "I am working on tract number 1760 of 2796 tracts\n",
      "I am working on tract number 1780 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1787 3 510781.3682259785 2.4503\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1787 3 76948.3545646437 1.2251\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1787 3 532761.1749983032 2.636\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1787 3 460465.3397177062 1.9306\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1787 3 118669.4852998543 1.5779\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1787 3 256265.96913760682 1.7542\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1787 3 162482.67020971805 1.666\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1787 3 205985.5965344879 1.7101\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1787 3 231437.9088770956 1.7322\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1787 3 244306.635354253 1.7432\n",
      "I am working on tract number 1800 of 2796 tracts\n",
      "I am working on tract number 1820 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1822 6.0 3 36.2 2.2187 369981.6\n",
      "we have 2 non-opposing shorted wedges for tract no 1831\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 3.0 0 102.5 1.0136 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 4.0 0 102.5 0.9816 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 5.0 0 102.5 0.9506 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 6.0 0 102.5 0.9207 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 7.0 0 102.5 0.8916 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 8.0 0 102.5 0.8635 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 9.0 0 102.5 0.8363 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 10.0 0 102.5 0.8099 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 11.0 0 102.5 0.7843 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 12.0 0 102.5 0.7596 199589.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1837 13.0 0 102.5 0.7357 199589.0\n",
      "I am working on tract number 1840 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1841\n",
      "I am working on tract number 1860 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1873 4.0 3 90.0 1.0537 202218.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1873 5.0 3 90.0 1.0104 202218.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1873 6.0 3 90.0 0.9688 202218.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1873 7.0 3 90.0 0.929 202218.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1873 8.0 3 90.0 0.8908 202218.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1875 1 448921.7541079406 1.6586\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1875 1 29576.469498447725 0.8293\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1875 1 449909.9539154523 1.6742\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1875 1 343924.1088085354 1.2517\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1875 1 66792.37950031791 1.0405\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1875 1 197087.79065952823 1.1461\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1875 1 267869.6518544165 1.1989\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1875 1 231597.32735573698 1.1725\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1876 2.0 3 90.0 0.8208 278023.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1878 3.0 3 46.9 0.9037 267544.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1879 5.0 0 108.4 1.1118 231848.7\n",
      "I am working on tract number 1880 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1880 5.0 0 116.1 1.1248 246795.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1880 1 442900.9256345366 1.736\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1880 1 35380.89407926286 0.868\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1880 1 440168.91019450425 1.6763\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1880 1 375384.38437949587 1.2721\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1880 1 107735.71348453365 1.0701\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1880 1 238187.63172985794 1.1711\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1880 1 163452.45649414806 1.1206\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1880 1 197911.4736904705 1.1458\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1880 1 179974.11513123725 1.1332\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1886 5.0 0 90.3 0.6138 192277.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1887 2.0 2 90.0 1.0241 196926.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1887 3 462323.9872532523 1.7727\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1887 3 36445.04398453038 0.8863\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1887 3 462325.75811901106 1.7727\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1887 3 273584.32975543186 1.3295\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1887 3 68742.36900094035 1.1079\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1887 3 137412.89067651608 1.2187\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1887 3 197338.39591590926 1.2741\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1887 3 162312.75999686128 1.2464\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1887 3 178553.36457630072 1.2603\n",
      "we have 2 non-opposing shorted wedges for tract no 1891\n",
      "I am working on tract number 1900 of 2796 tracts\n",
      "I am working on tract number 1920 of 2796 tracts\n",
      "I am working on tract number 1940 of 2796 tracts\n",
      "I am working on tract number 1960 of 2796 tracts\n",
      "I am working on tract number 1980 of 2796 tracts\n",
      "I am working on tract number 2000 of 2796 tracts\n",
      "I am working on tract number 2020 of 2796 tracts\n",
      "I am working on tract number 2040 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2048\n",
      "we have 2 non-opposing shorted wedges for tract no 2050\n",
      "we have 2 non-opposing shorted wedges for tract no 2052\n",
      "we have 2 non-opposing shorted wedges for tract no 2054\n",
      "we have 2 non-opposing shorted wedges for tract no 2057\n",
      "we have 2 non-opposing shorted wedges for tract no 2058\n",
      "we have 2 non-opposing shorted wedges for tract no 2059\n",
      "I am working on tract number 2060 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2064 3 196099.90766353457 0.5978\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2064 3 9403.38538280438 0.2989\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2064 3 200234.00408562127 0.6284\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2064 3 106025.48342901711 0.4637\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2064 3 185390.63686685835 0.546\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2067 3 18865.037558450073 0.689\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2067 3 584522.4763304142 2.4169\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2067 3 436430.55542295444 1.553\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2067 3 93268.13270620856 1.121\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2067 3 360031.3119721913 1.337\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2067 3 217349.9581061944 1.229\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2067 3 281633.94239538774 1.283\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2067 3 247583.12978842796 1.256\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2067 3 266494.82334969414 1.2695\n",
      "we have 2 non-opposing shorted wedges for tract no 2070\n",
      "I am working on tract number 2080 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2085\n",
      "we have 2 non-opposing shorted wedges for tract no 2086\n",
      "we have 2 non-opposing shorted wedges for tract no 2087\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2092 2.0 2 90.0 0.8086 286329.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2092 5.0 1 54.7 0.9697 250356.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2092 6.0 1 54.7 0.8607 250356.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2092 7.0 1 54.7 0.764 250356.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2094 2 456618.02122229285 2.4252\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2094 2 64764.72969832743 1.2126\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2094 2 457255.34122580406 2.4321\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2094 2 429723.74535546603 1.8224\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2094 2 105890.71498748654 1.5175\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2094 2 248699.78636824808 1.6699\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2094 2 162348.59327726002 1.5937\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2094 2 202843.9789187845 1.6318\n",
      "14 yoyos for tract,wedge,wedgePop,r= 2094 2 180244.53554771727 1.6128\n",
      "I am working on tract number 2100 of 2796 tracts\n",
      "I am working on tract number 2120 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2132\n",
      "I am working on tract number 2140 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2142 0 404623.7518477142 1.9952\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2142 0 35092.334128460265 0.9976\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2142 0 402013.4580270247 1.8758\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2142 0 388337.06646134733 1.4367\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2142 0 138430.21037344664 1.2172\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2142 0 292173.88933136594 1.3269\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2142 0 203333.91380547432 1.272\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2142 0 170431.2918170173 1.2446\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2142 0 185942.65346774497 1.2583\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2145 3.0 2 90.0 0.9672 268328.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2148 3.0 0 83.4 0.9298 251849.4\n",
      "we have 2 non-opposing shorted wedges for tract no 2152\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2156 13.0 3 88.6 1.5127 222977.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2156 18.0 3 88.6 1.5937 222977.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2156 3 75960.72301258106 0.7319\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2156 3 222976.95379992822 2.428\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2156 26.0 3 88.6 1.58 222977.0\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2156 3 222976.95379992822 1.58\n",
      "I am working on tract number 2160 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2162\n",
      "we have 2 non-opposing shorted wedges for tract no 2164\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2168 9.0 0 110.5 1.7138 273987.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2168 14.0 0 110.5 1.7246 273987.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2168 19.0 0 110.5 1.7208 273987.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2168 0 273987.4624387446 1.9261\n",
      "I am working on tract number 2180 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2199 13.0 0 108.1 1.6967 272753.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2199 18.0 0 108.1 1.7002 272753.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2199 0 272753.32718418067 1.903\n",
      "I am working on tract number 2200 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2204 2.0 1 90.0 1.0332 194072.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2204 3.0 1 90.0 1.022 194072.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2204 4.0 1 90.0 1.011 194072.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2204 5.0 1 90.0 1.0001 194072.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2204 6.0 1 90.0 0.9892 194072.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2212 1 252507.988741993 0.8916\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2212 1 44605.88254650688 0.4458\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2212 1 282165.2428209698 0.9421\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2212 1 67079.47936075676 0.694\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2212 1 153061.35980536375 0.818\n",
      "I am working on tract number 2220 of 2796 tracts\n",
      "I am working on tract number 2240 of 2796 tracts\n",
      "I am working on tract number 2260 of 2796 tracts\n",
      "I am working on tract number 2280 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2281 1 358857.61163803074 1.5835\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2281 1 92692.73167929164 0.7918\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2281 1 359390.6554072039 1.5939\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2281 1 222357.3690871991 1.1928\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2281 1 348275.9488594063 1.3933\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2281 1 333676.4503782217 1.2931\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2281 1 291132.1986847788 1.243\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2281 1 254400.40050671517 1.2179\n",
      "we have 2 non-opposing shorted wedges for tract no 2295\n",
      "I am working on tract number 2300 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2304 13.0 3 89.2 1.5139 223007.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2304 18.0 3 89.2 1.5105 223007.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2304 23.0 3 89.2 1.5101 223007.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2304 3 89380.67409099513 0.6745\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2304 3 223007.57428121084 2.2897\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2304 27.0 3 89.2 1.4821 223007.6\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2304 3 223007.57428121084 1.4821\n",
      "I am working on tract number 2320 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2324 4.0 3 36.3 1.8493 347303.6\n",
      "we have 2 non-opposing shorted wedges for tract no 2330\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2332 0 197906.94036998696 0.6352\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2332 0 4173.882923940662 0.3176\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2332 0 198586.59399575394 0.6801\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2332 0 117312.03481112995 0.4988\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2332 0 194774.25678876328 0.5895\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2332 0 175397.3634000694 0.5441\n",
      "I am working on tract number 2340 of 2796 tracts\n",
      "I am working on tract number 2360 of 2796 tracts\n",
      "I am working on tract number 2380 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2386 21.0 0 119.1 1.9923 363104.0\n",
      "I am working on tract number 2400 of 2796 tracts\n",
      "I am working on tract number 2420 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2435\n",
      "we have 2 non-opposing shorted wedges for tract no 2436\n",
      "we have 2 non-opposing shorted wedges for tract no 2439\n",
      "I am working on tract number 2440 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2446 2 303724.29137622716 1.742\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2446 2 76243.50214103312 0.871\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2446 2 428736.4113434253 1.8925\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2446 2 97732.21899315965 1.3818\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2446 2 154684.2774536189 1.6372\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2446 2 333040.01562137064 1.7648\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2446 2 243325.8505816851 1.701\n",
      "I am working on tract number 2460 of 2796 tracts\n",
      "I am working on tract number 2480 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2481 0 356661.45526258263 1.9635\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2481 0 79219.47938632598 0.9818\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2481 0 356674.68982794316 1.9649\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2481 0 251529.925342209 1.4733\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2481 0 99854.54489408634 1.2275\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2481 0 131376.6137185217 1.3504\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2481 0 182782.74052225152 1.4119\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2490 7.0 0 86.8 1.3429 197793.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2490 8.0 0 86.8 1.3095 197793.8\n",
      "I am working on tract number 2500 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2501 4.0 1 38.2 1.9387 374133.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2501 5.0 1 38.2 1.8984 374133.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2501 6.0 1 38.2 1.8589 374133.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2501 7.0 1 38.2 1.8202 374133.6\n",
      "we have 2 non-opposing shorted wedges for tract no 2511\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2514 3.0 0 90.0 1.3049 209801.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2517 6.0 3 41.3 1.1758 243661.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2517 7.0 3 41.3 1.1715 243661.0\n",
      "I am working on tract number 2520 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2521 10.0 3 91.4 1.5501 246293.9\n",
      "I am working on tract number 2540 of 2796 tracts\n",
      "I am working on tract number 2560 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2576\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2577 3 451578.434021903 2.5766\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2577 3 71569.1739061714 1.2883\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2577 3 432564.6872550801 2.1687\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2577 3 413824.02417678514 1.7285\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2577 3 306585.1664734645 1.5084\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2577 3 168850.24456448221 1.3983\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2577 3 227840.11483982165 1.4534\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2577 3 263979.7727886713 1.4809\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2577 3 243775.92892025562 1.4671\n",
      "I am working on tract number 2580 of 2796 tracts\n",
      "I am working on tract number 2600 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2618\n",
      "I am working on tract number 2620 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2629\n",
      "I am working on tract number 2640 of 2796 tracts\n",
      "I am working on tract number 2660 of 2796 tracts\n",
      "I am working on tract number 2680 of 2796 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2682\n",
      "I am working on tract number 2700 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2702 2.0 2 90.0 0.8249 300713.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2704 2.0 3 90.0 0.8813 282953.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2704 3.0 3 90.0 0.6463 282953.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 2.0 1 90.0 1.0201 195545.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 3.0 1 90.0 1.0033 195545.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 4.0 1 90.0 0.9868 195545.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 5.0 1 90.0 0.9706 195545.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2709 3.0 0 115.5 0.9641 225477.0\n",
      "I am working on tract number 2720 of 2796 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2720 3 408716.10724865255 2.497\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2721 3 456781.62681305065 2.3332\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2721 3 51401.1681343824 1.1666\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2721 3 456781.6268130539 2.3787\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2721 3 334459.2731224949 1.7727\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2721 3 84117.31925539521 1.4696\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2721 3 149248.8358201234 1.6211\n",
      "we have 2 non-opposing shorted wedges for tract no 2726\n",
      "I am working on tract number 2740 of 2796 tracts\n",
      "I am working on tract number 2760 of 2796 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2779 2.0 1 90.0 0.6904 286642.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2779 3.0 1 90.0 0.5008 286642.8\n",
      "I am working on tract number 2780 of 2796 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.000068\n",
      "Average and max number of wedgePop loops per tract were:  6.233547925608011 34.0\n",
      "min,average,max of Home District areas were:  0.0 1.1275806437620532 4.161424087907635\n",
      "calculated statewide vote was 0.5042743902606279, should have been 0.498716661561674\n",
      "calcd statewide Hispanic pop was 0.08704434258993136, should have been 0.09037508968679146\n",
      "calcd statewide Black pop was 0.28851015451688633, should have been 0.3027170383858251\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.30221745350500717\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.000068\n",
      "Average and max number of wedgePop loops per tract were:  6.233547925608011 34.0\n",
      "min,average,max of Home District areas were:  0.0 1.1275806437620532 4.161424087907635 for  GA\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start GA 9:19, end 11:04pm\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GA 17Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"17Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.608217080266525 906 pop,GOP 0.26269315673289184\n",
      "339 0.7383547686466977 1337 pop,GOP 0.6170531039640987\n",
      "340 0.7315863770720255 1295 pop,GOP 0.5922779922779923\n",
      "341 0.7224284182397344 387 pop,GOP 0.39276485788113696\n",
      "342 0.7533561814753774 828 pop,GOP 0.6123188405797102\n",
      "369 0.7598404727358525 2778 pop,GOP 0.775377969762419\n",
      "371 0.7858052847439598 2952 pop,GOP 0.7269647696476965\n",
      "372 0.7981341683302331 2908 pop,GOP 0.8304676753782668\n",
      "374 0.7976899768146026 2138 pop,GOP 0.14172123479887747\n",
      "401 0.7894985190947836 1193 pop,GOP 0.8189438390611903\n",
      "550 0.7840006274863636 714 pop,GOP 0.8837535014005602\n",
      "551 0.793816935710669 1125 pop,GOP 0.7848888888888889\n",
      "660 0.7958598580713467 754 pop,GOP 0.6763925729442971\n",
      "782 0.7854994770307537 507 pop,GOP 0.8303747534516766\n",
      "864 0.7665677121727441 1251 pop,GOP 0.1574740207833733\n",
      "865 0.77345551514671 1246 pop,GOP 0.2680577849117175\n",
      "874 0.5982117955441943 640 pop,GOP 0.178125\n",
      "1006 0.7887321747045569 1372 pop,GOP 0.11005830903790087\n",
      "1007 0.6811594887267218 1174 pop,GOP 0.5979557069846678\n",
      "1008 0.7307300591318743 545 pop,GOP 0.062385321100917435\n",
      "1009 0.770069295643494 1433 pop,GOP 0.16817864619678996\n",
      "1010 0.6658630018533147 1275 pop,GOP 0.4533333333333333\n",
      "1012 0.7282913983016326 972 pop,GOP 0.15329218106995884\n",
      "1013 0.7187982025861767 1073 pop,GOP 0.47530288909599255\n",
      "1017 0.7350891896847809 537 pop,GOP 0.020484171322160148\n",
      "1019 0.7626262259914884 783 pop,GOP 0.04342273307790549\n",
      "1020 0.7076915346635844 649 pop,GOP 0.4530046224961479\n",
      "1021 0.7228490005028444 888 pop,GOP 0.2072072072072072\n",
      "1027 0.700207346091824 643 pop,GOP 0.24883359253499224\n",
      "1127 0.7014614769939734 1138 pop,GOP 0.6845342706502636\n",
      "1128 0.6877486494921342 270 pop,GOP 0.5851851851851851\n",
      "1130 0.6743146775905023 327 pop,GOP 0.12232415902140673\n",
      "1138 0.7723203048544258 1385 pop,GOP 0.7270758122743682\n",
      "1222 0.6697857725695499 1415 pop,GOP 0.662190812720848\n",
      "1223 0.7040528934399011 1915 pop,GOP 0.6026109660574412\n",
      "1224 0.722539012218194 605 pop,GOP 0.2231404958677686\n",
      "1225 0.702870492754954 377 pop,GOP 0.7320954907161804\n",
      "1231 0.6668433804083113 1378 pop,GOP 0.7242380261248186\n",
      "1232 0.6637046534288887 234 pop,GOP 0.688034188034188\n",
      "1233 0.6879911319476152 1660 pop,GOP 0.5590361445783133\n",
      "1238 0.7384723388065894 2548 pop,GOP 0.3402668759811617\n",
      "1242 0.7412098134064103 1245 pop,GOP 0.26987951807228916\n",
      "1248 0.7737471343947768 1802 pop,GOP 0.683684794672586\n",
      "1249 0.7741327661423018 1633 pop,GOP 0.6025719534598898\n",
      "1250 0.7009806131831883 2423 pop,GOP 0.5988444077589765\n",
      "1251 0.7256786523528811 634 pop,GOP 0.23186119873817035\n",
      "1257 0.7543694733349237 1021 pop,GOP 0.8266405484818805\n",
      "1422 0.769463674512704 2238 pop,GOP 0.43655049151027703\n",
      "1432 0.6871697160705996 3000 pop,GOP 0.46266666666666667\n",
      "1433 0.12066941939391528 1942 pop,GOP 0.39752832131822863\n",
      "1482 0.667643234898921 1797 pop,GOP 0.6048970506399555\n",
      "1483 0.6268207945008778 405 pop,GOP 0.47901234567901235\n",
      "1484 0.7473984630915964 778 pop,GOP 0.033419023136246784\n",
      "1486 0.6523697106620937 1382 pop,GOP 0.6164978292329957\n",
      "1490 0.7196066588660115 538 pop,GOP 0.3754646840148699\n",
      "1493 0.6525054392361502 1364 pop,GOP 0.3709677419354839\n",
      "1496 0.6344825506338939 2836 pop,GOP 0.4192524682651622\n",
      "1497 0.6624237528738157 509 pop,GOP 0.2455795677799607\n",
      "1503 0.7482056994281043 613 pop,GOP 0.1598694942903752\n",
      "1550 0.7866626627863141 787 pop,GOP 0.8678526048284625\n",
      "1683 0.7895392158869432 904 pop,GOP 0.5486725663716814\n",
      "1823 0.7236293886965405 1751 pop,GOP 0.6773272415762421\n",
      "1826 0.7771089312726979 1061 pop,GOP 0.6437323279924599\n",
      "1828 0.7321834665695035 1427 pop,GOP 0.6748423265592152\n",
      "1830 0.7019760640975162 1608 pop,GOP 0.6237562189054726\n",
      "1831 0.7764096013009915 1364 pop,GOP 0.6099706744868035\n",
      "1836 0.7264787679479796 1636 pop,GOP 0.6222493887530562\n",
      "1957 0.6828585582362324 1637 pop,GOP 0.6951740989615149\n",
      "1960 0.7556537856234208 1562 pop,GOP 0.6421254801536491\n",
      "1961 0.7691745152918442 1046 pop,GOP 0.7724665391969407\n",
      "1966 0.6394015752085072 2279 pop,GOP 0.6770513383062747\n",
      "1969 0.6642890410781698 1666 pop,GOP 0.6074429771908764\n",
      "1970 0.743807510016709 1882 pop,GOP 0.6928799149840595\n",
      "1971 0.7698877574333275 1201 pop,GOP 0.7119067443796836\n",
      "2096 0.7333645334865867 874 pop,GOP 0.7540045766590389\n",
      "2103 0.7475320667466241 749 pop,GOP 0.6568758344459279\n",
      "2106 0.7817005942631403 832 pop,GOP 0.7704326923076923\n",
      "2107 0.7485219099061509 849 pop,GOP 0.8857479387514723\n",
      "2110 0.7653167067948484 503 pop,GOP 0.7713717693836978\n",
      "2113 0.7801945271369874 413 pop,GOP 0.7627118644067796\n",
      "2120 0.7974854087763363 835 pop,GOP 0.7664670658682635\n",
      "2129 0.7793164981105218 3677 pop,GOP 0.05765569757954855\n",
      "2131 0.7893688669366442 364 pop,GOP 0.717032967032967\n",
      "2133 0.7647290560893019 619 pop,GOP 0.6752827140549273\n",
      "2134 0.7944508099609551 361 pop,GOP 0.6703601108033241\n",
      "2143 0.7701367313956078 69 pop,GOP 0.5652173913043478\n",
      "2150 0.7288346086242775 2118 pop,GOP 0.5278564683663833\n",
      "2154 0.695405753329215 1032 pop,GOP 0.5474806201550387\n",
      "2157 0.7025608053066632 159 pop,GOP 0.5094339622641509\n",
      "2264 0.7822070211965155 774 pop,GOP 0.7997416020671835\n",
      "2271 0.7688316850772083 1257 pop,GOP 0.36754176610978523\n",
      "2274 0.799051698938512 758 pop,GOP 0.7361477572559367\n",
      "2276 0.790709963047393 3946 pop,GOP 0.594272681196148\n",
      "2277 0.7677554968877028 619 pop,GOP 0.14378029079159935\n",
      "2319 0.7202309579107745 4438 pop,GOP 0.775574583145561\n",
      "2321 0.6777261407772267 914 pop,GOP 0.6487964989059081\n",
      "2323 0.6580290261221187 1229 pop,GOP 0.7697314890154597\n",
      "2324 0.7251657588661378 3653 pop,GOP 0.7418560087599233\n",
      "2330 0.7870411141068858 4935 pop,GOP 0.8285714285714286\n",
      "2331 0.7673506706925272 3860 pop,GOP 0.7663212435233161\n",
      "2358 0.7974457180598524 4497 pop,GOP 0.7785190126751167\n",
      "2362 0.7929440337169775 3757 pop,GOP 0.7769496939047112\n",
      "2372 0.7870149449818092 3097 pop,GOP 0.7875363254762674\n",
      "2442 0.7694962935171703 2690 pop,GOP 0.7394052044609666\n",
      "2444 0.7410716304754317 1867 pop,GOP 0.776111408677022\n",
      "2445 0.7712479358109833 892 pop,GOP 0.8004484304932735\n",
      "2447 0.6306445887112283 474 pop,GOP 0.8734177215189873\n",
      "2452 0.6872968557245729 820 pop,GOP 0.7670731707317073\n",
      "2454 0.7396633659524694 654 pop,GOP 0.8149847094801224\n",
      "2457 0.6858991125378653 1899 pop,GOP 0.8420221169036335\n",
      "2459 0.7528347735799791 927 pop,GOP 0.7864077669902912\n",
      "2460 0.7448422750739822 2139 pop,GOP 0.7732585320243104\n",
      "2464 0.6576571909823562 1324 pop,GOP 0.918429003021148\n",
      "2489 0.7649875190564177 2503 pop,GOP 0.5273671594087096\n",
      "2498 0.644019389964215 1396 pop,GOP 0.27363896848137537\n",
      "2499 0.7557389233812274 2349 pop,GOP 0.0519369944657301\n",
      "2500 0.6608552808532976 4401 pop,GOP 0.19631901840490798\n",
      "2501 0.6814618241999365 2777 pop,GOP 0.06769895570759812\n",
      "2502 0.76879554352371 3975 pop,GOP 0.5509433962264151\n",
      "2503 0.7828597163355537 227 pop,GOP 0.7180616740088106\n",
      "2504 0.6940553929711998 2944 pop,GOP 0.24320652173913043\n",
      "2508 0.7964837810925083 2175 pop,GOP 0.6680459770114943\n",
      "2664 0.7379519137354505 350 pop,GOP 0.5714285714285714\n",
      "2665 0.7817164182560445 309 pop,GOP 0.42071197411003236\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in GA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10863628791506763 = GA std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10743659255257866 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for GA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.3889\n"
     ]
    },
    {
     "data": {
      "image/png": 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WZxSqdgO7F2ynyUxVTS/U5y2VSRkHTM5YJmUcMDljmZRxwOSMZdhxLPZdfLPAmr73q4Fji9wHSdIYWOyA+gqwPsm6JC8HNgP7FrkPkqQxsKin+KrqVJL3AP9M7zbzO6rq4CLsesFOFy6xSRkHTM5YJmUcMDljmZRxwOSMZahxpOqMS0CSJC05nyQhSWqSASVJatJEB1SSjUkOJzmSZPtS92cUSZ5I8nCSh5LMLHV/zkWSO5KcTPJIX+3iJPuTPNa9rljKPr4Y84zjA0m+1c3LQ0netpR9fDGSrEnyuSSHkhxMcnNXH8c5mW8sYzUvSV6R5P4k/9aN44+7+jjOyXxjOec5mdhrUN1jlb5B32OVgBvH9bFKSZ4Apqtq7L6AmOQtwDPAx6vqyq72Z8BTVbWr+5+HFVX1vqXs59nMM44PAM9U1QeXsm/nIslKYGVVPZjkh4EHgOuB32D85mS+sfwaYzQvSQJcVFXPJHkZ8EXgZuBXGb85mW8sGznHOZnkI6gfPFapqr4HPP9YJS2yqvoC8NRp5U3Anm55D71fKk2bZxxjp6qOV9WD3fLTwCF6T3kZxzmZbyxjpXqe6d6+rPspxnNO5hvLOZvkgBr0WKWx+w+3TwGfTfJA9yiocXdZVR2H3i8Z4NIl7s8o3pPka90pwOZPwfRLshZ4A3AfYz4np40FxmxekixL8hBwEthfVWM7J/OMBc5xTiY5oF7UY5XGyJur6o3ALwPbutNNWnq3AT8BvB44DvzFkvbmHCR5FfBJ4L1V9d2l7s8oBoxl7Oalqp6rqtfTe8LOhiRXLnGXhjbPWM55TiY5oCbqsUpVdax7PQl8mt4pzHF2ort+8Px1hJNL3J+hVNWJ7h/j94G/Ykzmpbs28Engzqr6VFceyzkZNJZxnReAqvoO8Hl612zGck6e1z+WYeZkkgNqYh6rlOSi7gIwSS4Cfgl45IW3at4+YEu3vAW4Zwn7MrTnf3l0foUxmJfuIvbtwKGq+lDfqrGbk/nGMm7zkmQqyWu65QuBtwJfZzznZOBYhpmTib2LD6C7jfHD/P9jlXYubY+Gk+S19I6aoPd4qr8fp7EkuQu4mt4j908AtwD/AOwFfgx4Erihqpq+AWGecVxN75RFAU8Av/38NYNWJfkF4F+Bh4Hvd+X307t2M25zMt9YbmSM5iXJ6+jdBLGM3oHD3qr6kyQ/wvjNyXxj+VvOcU4mOqAkSeNrkk/xSZLGmAElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlq0v8B6lQ1RS4MG4wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00556 0.50427 HD avg vs true 0.49872\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.06302, should have been 0.06802\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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V9iS/CvwFsBdwcVXd2VV7kqRddboXTVVdC1zbZRstdDb9s4dZK/2EtdPXtdJPWDt9HWs/U/WU3z0lST3gVgWS1FO9CPjFtkTIwIebz29PctxK1DkKLfr6i00fb09yU5JjVqLOYbXd5iLJy5PsSPKWcdY3Sm36muR1STYnuTPJ34y7xlFo8W/3GUn+LMltTT/PXIk6h5Xk4iT3J9kyz+fjy6OqWtUPBj/g/i1wJLAvcBtw9G7HnAL8OYO1+a8Ablnpujvs66uAQ5rnJ6/Gvrbp56zj/prB7zxvWem6O/w7PRi4Czi8ef3sla67o37+O+D9zfMJ4O+AfVe69mX09bXAccCWeT4fWx71YQTfZkuENwN/UAM3Awcnee64Cx2BRftaVTdV1XealzczuP5gtWm7zcXZwKeA+8dZ3Ii16esvAFdW1dcAqmo19rdNPws4MEmAH2QQ8NvHW+bwquoGBrXPZ2x51IeAb7MlQl+2TVhqP85iMFJYbRbtZ5LDgH8MfHSMdXWhzd/pPwAOSbIhycYkZ4ytutFp08/fBX6EwQWRdwDnVtUT4ylvrMaWR324ZV+bLRFabZuwCrTuR5LXMwj4EzqtqBtt+vlB4NeqasdgwLdqtenr3sDLgJOApwNfSHJzVf3ProsboTb9/ElgM3Ai8MPA9Uk+V1UPdVzbuI0tj/oQ8G22RGi1bcIq0KofSV4CXAScXFXfHlNto9Smn1PAJ5twPxQ4Jcn2qvqTsVQ4Om3//T5QVY8AjyS5ATgGWE0B36afZwIX1GCi+itJ7gX+IfDF8ZQ4NmPLoz5M0bTZEuHTwBnNr9evAB6sqm+Nu9ARWLSvSQ4HrgR+aZWN8GZbtJ9VdURVTVbVJHAF8I5VGO7Q7t/vnwKvSbJ3kv2BHwO2jrnOYbXp59cY/L8UkjwHOAq4Z6xVjsfY8mjVj+Brni0RkvyL5vOPMlhlcQrwFeBRBiOFVadlX98LPAv4vWZ0u71W2SZOLfvZC236WlVbk1wH3A48AVxUVXMuwdtTtfw7/U3gkiR3MJjG+LWqWnU7TCa5HHgdcGiS+4DzgX1g/HnklayS1FN9mKKRJM3BgJeknjLgJamnDHhJ6ikDXpJ6yoBXbyU5Nskp83z2uiRX7/beJTt3pUxyUZKjx1TntUkOHkdbWltW/Tp4aQHHMrjidcl3FauqXxl5NfO3Nef/CEnDcgSvVSPJZJIvJ7m02Uf7iubKzp37wt/U7CX+xSTPAH4DOK3ZR/20Jba1IclUkr2akf2WJHckedeszz/YtLklyfHN+8c3793a/HlU8/7bk1yZ5Lok/yvJ78xqa1uSQ5vnZzR9uy3JJ0bzX05rlSN4rTZHAWdV1eeTXAy8I8mHgf8OnFZVX0pyEIMrBN8LTFXVr85zrtck2Tzr9eHA1bsdcyxwWFW9GGC3qZQDqupVSV4LXAy8GPgy8Nrmys03AL8F/Oysc70UeAy4O8mFVfX/dxVM8qPAe4BXV9UDSZ7Z+r+KNAcDXqvN16vq883zPwTOYXD5+7eq6ksAO3cfbLHL5Oeq6k07XyS5ZI5j7gGOTHIhcA3wmVmfXd60d0OSg5rwPxC4NMmLGOwQuM+s4/+qqh5s2roLeAG7bht7InDFzsvzq2qhPcWlRTlFo9Vm9701isG+JZ3sudHcPOUYYAPwrxjs0rlQLb8JfLYZ8f8UsN+szx+b9XwHTx1gddYPrU0GvFabw5O8snl+OnAjg2mR5yV5OUCSA5PsDTzMYES9bM3c+NOq6lPAv2dwK7adTmuOOYHBjoAPAs8AvtF8/vYlNvdXwM8neVZzXqdoNBQDXqvNVuCfJrkdeCbw+80t4E4DLkxyG3A9g5HzZ4Gjl/Mj6yyHARuaufpLgF+f9dl3ktzE4K5SZzXv/Q7w20k+z2DXxNaq6k7gfcDfNP34wDJrlgB3k9QqkmQSuHrnD54rXMsG4N9U1fRK1yLNxxG8JPWUI3hJ6ilH8JLUUwa8JPWUAS9JPWXAS1JPGfCS1FMGvCT11P8DzdmNECocqRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for GA\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hmt5RVWd1VYckafe6PCJYAWytqm9U1XeBzwBnd7g/SdJe6DIIDgYeHprf1iwbdVKSTUluSfL6cRtKsjLJuiTrtm/f3kWtktRbXQZBxiyrkfkNwGFVdQxwBXDDuA1V1ZqqWl5Vy6empma3SknquS6DYBtw6ND8IcAjww2q6qmqeqaZXgssTrKkw5okSSO6DIKvAEcmOTzJS4FzgRuHGyR5TZI00yuaep7osCZJ0ojO7hqqqh1J3gPcCiwCrq6qB5K8q1m/GjgHuCjJDuB54NyqGj19JEnqUKfDUDene9aOLFs9NL0KWNVlDZKk3fPJYknqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQpJ4zCCSp5wwCSeo5g0CSes4gkKSeMwgkqecMAknqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQpJ7r9J3FSU4DPsrg5fUfr6rLRtanWX8G8Bzwjqra0GVNkrqx7JKbJ7avhy47c2L76oPOjgiSLAKuBE4HjgLOS3LUSLPTgSObz0rgqq7qkSSN1+WpoRXA1qr6RlV9F/gMcPZIm7OBa2vgbuCAJAd1WJMkaUSqqpsNJ+cAp1XVLzXz5wMnVNV7htrcBFxWVXc2838O/HpVrRvZ1koGRwwArwW+vhclLQEe34vvzWd97DP0s9997DP0s9972+fDqmpq3IourxFkzLLR1GnThqpaA6zZp2KSdVW1fF+2Md/0sc/Qz373sc/Qz3530ecuTw1tAw4dmj8EeGQv2kiSOtRlEHwFODLJ4UleCpwL3DjS5kbgggycCDxZVY92WJMkaURnp4aqakeS9wC3Mrh99OqqeiDJu5r1q4G1DG4d3crg9tELu6qHfTy1NE/1sc/Qz373sc/Qz37Pep87u1gsSZoffLJYknrOIJCknltwQZDktCRfT7I1ySVj1ifJ5c36zUmOn4s6Z1OLPv9809fNSe5Kcsxc1DmbZurzULs3J9nZPNcy77Xpd5KTk2xM8kCS/zbpGmdbi/+/X5nkvyTZ1PS5y2uNE5Hk6iSPJbl/mvWz+3usqhbMh8FF6f8JHAG8FNgEHDXS5gzgFgbPMJwI3DPXdU+gz38fOLCZPr0PfR5q9xcMbko4Z67rntC/9QHAV4Glzfyr57ruCfT5g8DvNdNTwDeBl8517fvY738AHA/cP836Wf09ttCOCPo4rMWMfa6qu6rqW83s3Qye15jP2vw7A7wX+Bzw2CSL61Cbfv8ccH1V/TVAVc33vrfpcwH7N4NYvpxBEOyYbJmzq6puZ9CP6czq77GFFgQHAw8PzW9rlu1pm/lkT/vziwz+kpjPZuxzkoOBfwysnmBdXWvzb/33gAOTfCnJ+iQXTKy6brTp8yrgxxg8jHof8P6qemEy5c2ZWf091ukw1HNg1oa1mEda9yfJKQyC4C2dVtS9Nn3+CINxq3YO/lBcENr0+yXAm4CfAF4GfDnJ3VX1P7ouriNt+vwPgY3A24AfAW5LckdVPdVxbXNpVn+PLbQg6OOwFq36k+SNwMeB06vqiQnV1pU2fV4OfKYJgSXAGUl2VNUNE6mwG23//368qp4Fnk1yO3AMMF+DoE2fL2QweGUBW5M8CLwOuHcyJc6JWf09ttBODfVxWIsZ+5xkKXA9cP48/stw2Ix9rqrDq2pZVS0D/gS4eJ6HALT7//vzwFuTvCTJDwAnAFsmXOdsatPnv2ZwBESSH2IwQvE3Jlrl5M3q77EFdURQ33vDWnSuZZ8vBX4Q+FjzF/KOmscjNrbs84LTpt9VtSXJF4DNwAsM3gw49hbE+aDlv/VvA59Kch+DUya/XlXzemjqJJ8GTgaWJNkGfAhYDN38HnOICUnquYV2akiStIcMAknqOYNAknrOIJCknjMIJKnnDAJpBkmOTXLGNOtOTvJkM9rn5iT/Ncmrm3XvSLJqL/f5zL7ULO0Jg0Ca2bEM7tmezh1VdWxVvZHBA1DvnkhV0iwxCNQ7SZYl+VqSa5q/4v+keQp31/sL7mrGtr83ySuBDwNvb/7qf/tuthtgf+BbY9b9dJJ7kvxlc9TwQ83ylyf5ZJL7mlp+duR7S5J8OcmZs/nfQBpmEKivXgusaf6Kfwq4uBnC4LMMRq88BjgVeJbBk9mfbf7q/+yYbb01yUYGQx2cClw9ps2dwIlVdRyDoZR/rVn+rxkMD/CGppa/2PWFJixuBi6tqpv3ucfSNAwC9dXDVfXfm+k/YjAi62uBR6vqKwBV9VRVtRnXftepoUOBTwK/P6bNIcCtzTAI/wp4fbP8VODKXY2G3huxGPhz4Neq6rY965q0ZwwC9dXo2CrFYJyafR1z5UYGb5cadQWwqqreAPwysF+zfLp97gDWMxhiWeqUQaC+WprkpGb6PAanbr4G/HCSNwMk2T/JS4CnGZz7b+MtDF6tOOqVwN80078wtPzPgPfsmklyYDNZwDuB12U372SWZoNBoL7aAvxCks3Aq4Crmlchvh24Iskm4DYGf7l/EThqNxeL39qs2wScD3xgTJvfBP5zkjuA4ZExf4fBG8Xub75/yq4VVbWTwbDLpyS5eB/7K03L0UfVO0mWATdV1dFzXYv0vcAjAknqOY8IJKnnPCKQpJ4zCCSp5wwCSeo5g0CSes4gkKSe+7+ZDM3mnkFK2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the GA map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  765136.2142857143\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ms6q6VlUPsXSGmu1JPj3ikcZWks8Bl6vq1KhnWc4kBMrTKqlpSe5kKU7fraofjHqeSVFVvwNewfdL1+JR4PNJ3mbp7ZHHknxntCP9v0kIlKdVUrOSBPgWcLaqvj7qecZdkpkkn+y2Pw58BnhjpEONsao6UFWbq2qWpe+dP6qqL454rD8Z+0BV1VXgg9MqnQWO3qbTKk2sJN8DfgL8dZKFJHtGPdMYexT4Eks/mb7e/frsqIcaYxuBl5P8gqUfTo9XVVMfjdbgeKojSVKTxv4ISpI0mQyUJKlJBkqS1CQDJUlqkoGSJDXJQEmSmmSgJElN+j8Fv5bOTEXqagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0aE477TRefvllHn/8cR566KFEnGZUWOnr5NT/ha/4e93XPOSZQsu0/MtAA2RrCrNyOlgfQJKwFkEULFq0iPr161OvXj3S0tLo168fM2fOzLXf5MmT6dWrF7Vr1wagevXqANSoUYOWLVsCUKFCBZo0acLGjRsBWLVqFaeeeioAZ555pr+q6YQJE+jVqxdnn302DRo0YNiwYf7XeeONN2jevDknnHACt99+e9iY77jjDpo2bUqLFi249dZbARgwYADXXnstnTt3pl69enz22WdcddVVNGnShAEDBvif+7///Y8OHTrQsmVL+vTpw+7duwGnimqbNm044YQTGDRoEL6bFTt16sRdd93FaaedFtTaMUWfryJox/VjeStMCwCCWwD71cNz2efRYP9r3JJ9nbUAkkSx/GrWqVPudRddBEOGwN690K1b7u0DBjg/W7fChRcGb5s3L//X27hxI7Vq1fIvp6ens3Dhwlz7rV69mgMHDtCpUyf++usvbrrpJi6/PHikyfr168nMzKRdu3YAnHDCCcyaNYuePXsybdq0oDpES5cuJTMzk9KlS9OoUSNuuOEGPB4Pt99+O0uWLKFy5cp07dqVGTNmcP755/uft337dt555x1+/PFHRIQdO3b4t/3555988sknzJo1i+7duzN//nxefPFF2rRpw9KlS0lPT2fEiBF8/PHHlC9fnkcffZQnn3yS++67j+uvv5777rsPgMsuu4z33nvPXzF1x44dfPbZZ/m/kabICCwHMTd1XFAZaJ9wReBsGGhyKpaJIN7ClemQ0MIpOJdFlixZwty5c/n777/p0KED7du3p2FDZz7V3bt307t3b8aMGcORRx4JwMsvv8yNN97Igw8+SI8ePUhLS/Mfr0uXLlSsWBGApk2b8ssvv7Bt2zY6depEtWrVAOjfvz+ff/55UCI48sgjKVOmDAMHDuTcc8/lvPPO82/r3r07IkLz5s05+uijad7c+Y/crFkz1q9fT1ZWFt9//z0dO3YEYP/+/XTo4MwA9emnn/LYY4+xd+9etm/fTrNmzfyJoG/fvoV7c5NFlSp4A6rFJrN2I+ew+a/9PJH6DL088/3rw3UC79E0RmRf5h8BBJYAklGxTAT5fYMvVy7/7VWrFtwCCJWenh70TT0rK4tjjz027H5Vq1alfPnylC9fnlNPPZVly5bRsGFDDhw4QO/evenfv3/QnASNGzfmf//7H+C0KN5//33/ttDS19nZ2WGTUqjU1FQWLVrE3LlzmTJlCmPHjuWTTz4JOmZg2WvfcnZ2Nh6PhzPPPJM33ngj6Jj79u1jyJAhLF68mFq1ajF8+HD27dvn317sS19Pn87WJL+PoP8LXzH/p+20lNVMT32amp7tQPhWQOicwGAJIJlZH0EUtGnThjVr1vDzzz+zf/9+pkyZQo8ePXLt17NnT7744guys7PZu3cvCxcupEmTJqgqV199NU2aNOHmm28Oeo6vQzknJ4cRI0YwePDgfGNp164dn332GVu3bsXr9fLGG2/kKn29e/dudu7cSbdu3RgzZkzElU4B2rdvz/z581m7di0Ae/fuZfXq1f4P/apVq7J7927eeuutiI9pEiuwH2B52pVMTxueKwkEzgb2trcjjfdP9CeBjscfZX0ASS6mLQIRORt4CvAAL6rqIyHbKwKvAbXdWEarau46zUVcamoqY8eO5ayzzsLr9XLVVVfRrJlTjnjcuHEADB48mCZNmnD22WfTokULUlJSGDhwICeccAJffvklkyZNonnz5v5Zzh5++GG6devGG2+8wTPPPANAr169uPLKK/ONpUaNGowaNYrOnTujqnTr1o2ePXsG7fPXX3/Rs2dP9u3bh6ryn//8J+JzrVatGhMmTODiiy/2z4Y2YsQIGjZsyDXXXEPz5s2pU6cObdq0ifiYxcKdd1Ip81t2nNQy0ZFEzHdDWEtZzZcBLQAI3wpYl3M0t2Vfy7fqXMq0eYGLj5iVoRYRD7AaOBPIAr4BLlbV7wP2uQuoqKq3i0g1YBVwjKruD3dMsDLUxVGx+P0lWRlq33DQ0EnhwyUABcZln+dvAaSmCKP72KxgySZRZajbAmtVdZ0bxBSgJ/B9wD4KVBCnZ/UIYDuQHcOYjCnRfKOBhnkmMzjt4LwAeY0GCmwFpAg8eZHNCVAcxTIR1AQC51zMAtqF7DMWmAVsAioAfVU1J2QfRGQQMAjwj8E3xhyaFvd/SP39PxQ4HDRwQhirB1QyxDIR5B4/6fyNBToLWAqcDhwPzBGRL1Q1qI6Cqo4HxoNzaSj6oRpTfBXUCsirBQBOR/Dr13SIb8Am7mKZCLKAWgHL6Tjf/ANdCTyiTkfFWhH5GWgMLIphXMZEX3o62b/8mugocom0FeCbFN7XAji6QhoL7z4z3uGaBIllIvgGaCAidYGNQD/gkpB9fgW6AF+IyNFAI2BdDGMyJjZee41tReg+gsA7g99MG45vypzQVkBoR7BvZjDrByhZYpYIVDVbRK4HPsIZPvqyqq4UkcHu9nHAQ8AEEVmB8zd4u6pujVVMxhR3gUNCp6ROoXXKKjyEbwVsyqnMDdk32XBQE9sbylR1tqo2VNXjVXWku26cmwRQ1U2q2lVVm6vqCar6WizjibVISlGHK0Od33PvvfdeWrRoQUZGBl27dmXTptCra0XXgAEDSs6NZUOHUnnxNwkNof8LXzF06lKGeSb7C8R5AuYJCL0prOOBZ/hWG5KaIozpm2FJoAQrliUmEiGSUtR5laHO77m33Xabv+z0008/zYMPPui/SS0RvF4vHk/Jnps3rKVLSdv+Z0JeOnSmMF99oEhaAWPsMpDBSkxETSSlqPMqQ53fc33F5wD27NnjL2a3cuVK2rZtS0ZGBi1atGDNmjWsX7+exo0b++9Y7t+/Px9//DEdO3akQYMGLFq0yH+cq666ijZt2nDSSSf5X2v9+vWccsoptGzZkpYtW7JgwQLAacV07tyZSy65hObNm+P1erntttto06YNLVq04Pnnnwec4nvXX389TZs25dxzz/UnOhM7ga2AlWlX5EoCgSWiv/XW87cCGlQvz/pHzrUkYIDi2iL4uFPudbUvgoZDIHsvzAtTh7reAOdn31b4MqQO9RnzCnzJSEpR51WGuqDn3n333UycOJGKFSvy6aefAk7piptuuon+/fuzf/9+vF4vmzdvZu3atUybNo3x48fTpk0bJk+ezJdffsmsWbN4+OGHmTFjBiNHjuT000/n5ZdfZseOHbRt25YzzjiD6tWrM2fOHMqUKcOaNWu4+OKL8d3FvWjRIr777jvq1q3L+PHjqVixIt988w3//PMPHTt2pGvXrmRmZrJq1SpWrFjB5s2badq0KVdddVWB750pnHYj51Bz93d5lofwJYDQEtHWCjChimciSIBISlHnVYa6oOeOHDmSkSNHMmrUKMaOHcsDDzxAhw4dGDlyJFlZWfTq1YsGDRoAULdu3aDS0V26dPGXlV6/fj3gTCwza9YsRo8eDTiVQ3/99VeOPfZYrr/+epYuXYrH42H16tX+GNq2bUvdunX9z1++fLn/+v/OnTtZs2YNn3/+ORdffDEej4djjz2W008/vbBvp8mHb0RQv5S5jEx7yd+sD3cp6G1vR/8sYdYZbPJSPBNBft/gU8vlv71M1YhaAKEiKUWdVxnqSMtYX3LJJZx77rk88MADXHLJJbRr147333+fs846ixdffJF69erlKh0dWFY6O9up3qGqTJ8+nUaNGgUdf/jw4Rx99NEsW7aMnJwcypQp498WWEZaVfnvf//LWWedFfT82bNnh52HoURo2JAD7qxysRRuroCC6gPZkFBTEOsjiJJISlHnVYY6v+euWbPG//xZs2bRuHFjANatW0e9evW48cYb6dGjB8uXL4841rPOOov//ve//pZIZmYm4Hyzr1GjBikpKUyaNAmv15vn85977jkOHDgAOJe89uzZw6mnnsqUKVPwer389ttv/stYJcL48WxvH7s7cGdkbqTOHe+7l4Kuz7MvQIGVOcdx4f7hPOa9hAbVy/Oz9QWYAhTPFkEC5FWKOpIy1ECeZazvuOMOVq1aRUpKCscdd5z/eFOnTuW1116jVKlSHHPMMdx3333s2rUrfHAh7r33XoYOHUqLFi1QVerUqcN7773HkCFD6N27N9OmTaNz5855TiYzcOBA1q9fT8uWLVFVqlWrxowZM7jgggv45JNPaN68OQ0bNsw1D4IpHN+EMa+kjqKTZ4V/fWhfQGB5CN9cwZYATCRiVoY6VqwMdfFTLH5/gwbx16efsr19h6iWoW43cg6d93zA3Z5JHJHiVGe3vgBTGIkqQ21MybF6NaV2/RW1w/k6hJ9IfYZepfKeN9j6Akw0WCIwpog588l5rPljT9gO4bwqhVqROHM4ik0iUNWSO2IliSXbpclYazdyDlfsncAVaR9RTpzO+NAksEfTGJF9mc0VYKKmWCSCMmXKsG3bNqpUqWLJIImoKtu2bQsaplqS9btnDO8ymmqpBy8xBY0KAt4J6As4srSH5Q+cnYBITXFTLBJBeno6WVlZbNmyJdGhmENUpkwZ0tPTEx3G4cvIYP/mzYV66ozMjXinX8Mbntx9AXAwCdx14Gp/K8A6hE00FYtEUKpUKf9dr8YkxJgx/FmI+QjuH/sS1/w+wl8iIlxfADgdwlNyuliHsImJYpEIjElGrz5wOffnzETcYq6hSSBbhd+oyrPZPZiS08U6hE3MWCIwJhouvZQq8xew7eSTI9p9wfBTuVyXAeFbAd9669E7e4R/2eYONrFkicCYaMjKInXv3oh2XT68FR10LZA7CYTOHQx2h7CJPUsExsTLhkVserEvzQnfHxDaCijjEX4cGaZkujFRVmDRORHpJSJrRGSniOwSkb9EJLKiNsYYx4ZFZL90JjUCkkDo1JGBSeDoCmmWBEzcRNIieAzorqo/xDoYY4qlDYvY8dIFVNTgYaEAu3LKcmX27f6pI8H6A0z8RVKGerMlAWMK0KED/1Srmnv94gnkvHQmFXW3f5WvJeCFXEng0va1LQmYuIukRbBYRKYCM4B/fCtV9e1YBWVM0hk1ih1ZIRPTzLmfnPljEM2/ZLSPlYowiRJJIjgS2At0DVingCUCY/Iy/Rp0xZthk8A8b3OuzL4zaHcbGWQSqcBEoKpXxiMQY5Ja795U/WYxW087zZ8ECJMEvvXWsyRgipxIRg2li8g7IvKHiGwWkekiUgyKwxgTRdu24fnnH9j8XZ5JYJ63edDIIHAuB1kSMIkWSWfxK8As4FigJvCuu84YE2j/bvTvHbmSgALPZZ+XqyVgfQKmqIgkEVRT1VdUNdv9mQBUi3FcxiSX35ZBjhfInQTuOnA1j3kvCdrdkoApSiLpLN4qIpcCb7jLFwPbYhdSMfVxp9zral8EDYdA9l6YF+bmoXoDnJ99W+HLC3Nvb3AtHNcX9myAry7Lvb3xLZDeHXatgkX/l3v7CffAMWfAn0thydDc2098GKr9C7YsgGV35d7eagxUzoDfP4bvRuTe3vZ5OLIRZL0LPz6Re3uHSVC+FvwyFdY8l3v7yW9BmaqwboLzE6rTbEgtB6ufhV/fzL39jHnOvz+Mho3vBW/zlIXOHziPVzwEm+cGby9dBU6Z7jxeeids/Sp4e7l0+NdrzuOpJwA7SKsqHNPjN7TyAdifApvLMi77PFrU+J7zS8/xP/XIsqVoWutUYIyzYsGlsDcr+PhVO0DGKOfxF73hn5D/ckd3geb3Oo8/PQe8fwdvr3keNLnVeWx/e7m3H+7fnu9vq5iIpEVwFXAR8DvwG3Chu84Ys2ERbF2DNhG89T3+1QfwcGeYlkDZNA9NaxwZ7yiNyZcUNFWgiJRR1X1xiqdArVu31sWLFyc6DGMcz52Mbl6BAL/MrYICqacpJ2ePzbWrTSZjEklElqhq63DbImkRfCci80XkERHpJiIVD+GFzxaRVSKyVkTuyGOfTiKyVERWishnkR7bmISbcz9sXuF0BOD/hxu9N+ba1ZKAKcoKTASqWh+nX2AFcB6wTESWFvQ8EfEAzwDnAE2Bi0Wkacg+lYBngR6q2gzoc4jxG5MYiyfA/DEobgG51/ZQfeXvbNIqQXcL+1gSMEVZgZ3F7j0DHYFTgBOBlcCXERy7LbBWVde5x5kC9AS+D9jnEuBtVf0VQFX/OKTok0inTrnXXXQRDBkCe/dCtzD9dQMGOD9bt8KFYfrrrr0W+vaFDRvgsjD9dbfcAt27w6pV8H9h+uvuuQfOOAOWLoWhQ3Nvf/hh+Ne/YMECuCtMf92YMZCRAR9/DCPC9Nc9/zw0agTvvgtPhOmvmzQJatWCqVPhuTD9dW+9BVWrwoQJzk+o2bOhXDl49ll4M0xf8bx5zr+jR8N7IX3FZcvCB25f8UMPwdyQvuIqVWC621d8553wVWBf8T+7SN9Tlkm9QIChH45iQNYLeLNTGLjiJX7/tSaljtpDlbNXANBwdadcv/+MDOf9A7j0UsgK6Svu0AFGuX3FvXvDtpC+4i5d4F63r/icc+DvkL7i886DW92+Yvvby739cP/2fH9bxUUko4Z+Bb4BHlbVwYdw7JrAhoDlLKBdyD4NgVIiMg+oADylqhNDDyQig4BBALVr1z6EEIyJgW0/QRknCcDBS0I5SK5dOx5/FOW3l2f19rhFZ8whi6Sz+ETgZOBUoDawBvhMVV8q4Hl9gLNUdaC7fBnQVlVvCNhnLNAa6AKUBb4CzlXV1Xkd1zqLTUJNvADWfeJcEuLgXcObJpSh1I5sPj2hPbefMgSwfgFTtOTXWRxJraFlIvIT8BPO5aFLcZJCvokApwVQK2A5HdgUZp+tqroH2CMin+NcfsozERiTMHPuD0oCPr/mVOU3LUe9kD9vSwImWURSa2gxzjf1C4AfgVNVtU4Ex/4GaCAidUUkDeiHU6oi0EzgFBFJFZFyOJeObO4DU/RsWATznwIOtgR8rYHnvD2Ze3xbNlWu7t/90vZ2CdMkj0j6CM5R1S2HemBVzRaR64GPAA/wsqquFJHB7vZxqvqDiHwILAdygBdV9btDfS1jYm7ZGxzsDXDs1TQeyr7MmWi+HdTf/zvgXBKy8hEmmURyaeiQk0DAc2cDs0PWjQtZfhx4vLCvYUxcZH3jf6jqfGu57MBdNlTUFAuR3FBmTMm2YRH8viKoPbA259igJDBl8h10/u5r6lc/Iv7xGXOYLBEYU5D5T+UaJfSK95xcu6WmCFWPKB3X0IyJhkg6i/uISAX38T0i8raItIx9aMYUARsWwY/vB3UPrPQe5/QLhCiXFkmXmzFFTyQtgntV9S8RORk4C3gVCHMvnjHF0PynUNQ/xwDAUurn2u3oI8vEMShjoiuSROB1/z0XeE5VZwJpsQvJmCIipDXg6yR+23tK0G4pQN2q5eMenjHREkki2Cgiz+PMSTBbREpH+DxjktuyN3K1BuZ4W+caKfRk3wy46CL2HHdcfOMzJkoi+UC/COdegLNVdQdwFHBbLIMypkjYsipXa2C897ygXToef5Qz+fyQIexu1Cj+MRoTBZEkgudV9W1VXQOgqr8BYeoNGlOMbFiE95cFQavCtQZev6aD82DvXiQ7O17RGRNVkSSCZoEL7jwDrWITjjFFw7q5L5KizmUhVfAiuVoDQWUkunWj+iefxDlKY6Ijz0QgIneKyF9ACxHZ5f78BfyBUyPImGLrp3U/BS0v9jbK1RqwMhKmuMgzEajqKFWtADyuqke6PxVUtYqq3hnHGI2JqxmZG6mQszto3Q6C7xi2onKmOImk1tCdIlIZaACUCVj/eSwDMyZR3nxnOhM9Pwat28rBqbpTsNaAKV4imapyIHATznwCS4H2OGWpT49pZMYkyI36Oh7B3z8Qeu/Ak30zEhabMbEQyT3xNwFtgK9VtbOINAYeiG1Yxcujix7lx+0/FryjSbj1G3/juJrbeZ6DcwvspTQ/5HxKWT7FkyLM3HwUMz8Mfl7HVtm0+7Mcm7b/yPAPr4xz1CbeGh/VmNvb3p7oMKImklFD+1R1H4CIlFbVHwEbMG2Kna27/+HI/X/kmnn4dz3K/zivO4jnn9mATelVYxidMbETSYsgS0QqATOAOSLyJ7mnnDT5KE7fHIqzS+57monyPh4OXhZa6T2O87Kd31+pFHh3yLnhn7x1KxvKDSGnTBleOfuV+AVtTBRE0ll8gftwuIh8ClQEPsznKcYknRmZG+mWMw+PhzwLzD3eJyPvA1x4IdV++JHNXbvGLkhjYiSimkEicrKIXKmqn+F0FNeMbVjGxNfd76ygKjv9y6GdxKVScEpJGFMMRTIfwf3A7YDv3oFSwGuxDMqYeNuz30sDyQpa9423sf8msnxbA8YkuUhaBBcAPYA9AKq6CagQy6CMiaf+L3zFE6nPUC/l96D1a92Gb9lSKdYaMMVaJIlgv6oqbh1GEbHC66bYmJG5kb/Xfc0FnvnAwU5i5eBloVG9WiQwQmNiL5JE8KY7H0ElEbkG+Bh4IbZhGRMfd7+zgl6eLxAOJgGAd7wd+VYbRt43cO21/NWwYcH7GVMERTJqaLSInAnswrl/4D5VnRPzyIyJsRmZG9mz30v91I1B69d5j+GW7OuAQ+gb6NuXve+9H+UIjYmPSEpMlAc+UdU5ItIIaCQipVT1QOzDMyZ27n5nBS1lNa09q4LWf+VWXj+kkUIbNuDZswdvebtyapJPJJeGPgdKi0hNnMtCVwITYhmUMbHmaw308nyBh+B5B3x9A4c0Uuiyy6g6f35sgjUmxiJJBKKqe4FewH/dG8yaxjYsY2Lr7ndWAJAha4PW++YdsJFCpiSJKBGISAegP+C7CBpJaQpjiqw9+730S5lLs5Rfgtb7hozaSCFTkkSSCG7CuZnsHVVdKSL1gE9jG5YxsXPPDKc10NczD8g9ZNRaA6akiWTU0Oc4/QS+5XXAjbEMyphYeu3rXwH4R0sFrV/k3kk8xloDpoSJqNaQMcVF/xe+AqClrKaVZzVw8N6BGTkdC19T6JZb2NW0SbTCNCauLBGYEmNG5kbm/7QdgF6eL0glB/HNRAYcJbsLX1Ooe3f+Tq8VtViNiadIis4VerYNETlbRFaJyFoRuSOf/dqIiFdELizsaxlTkAfeXel/nLvSaArf0KTwfQOrVpG6c2fB+xlTBOWZCESku4hsAVaISJaI/OtQDiwiHuAZ4Byc4aYXi0iuYafufo8CHx1S5MYcoj/3OvdAtpTVdPF8Cxy8LDQ+uxv9L7yo8Af/v/+jysKFhxuiMQmRX4tgJHCKqtYAegOjDvHYbYG1qrpOVfcDU4CeYfa7AZgO/HGIxzcmYr6RQgCDPO/luiy0V8rbSCFTYuWXCLLd+YlR1YUceunpmsCGgOUsQia0ce9WvgAYl9+BRGSQiCwWkcVbtmw5xDCMOThSqKWs5gzPYv96Z9io0KZzuO8oxpQM+Q0frS4iN+e1rKpPFnDs0DnAwS1lHWAMcLuqekXC7e5/rfHAeIDWrVuHHsOYfPlGCgFuSYng6SjneFtxdpc85iI2pgTILxG8QHArIHS5IFlA4DCKdHJPet8amOImgapANxHJVtUZh/A6xuQpcKQQhOskhl+aDExAZMYUHXkmAlV94DCP/Q3QQETqAhuBfsAlIa9R1/dYRCYA71kSMNHkqynkky7Blxa/8Tbm//pffPgvdM897Hzk0cM/jjEJkO/wURE5R0Q+F5GtIrJFRD4TkW6RHFhVs4HrcUYD/QC86ZaoGCwigw8/dGPy56sw6jPMMzlXbaHqx0fpLuIzzmBfjRrROZYxcZZni8Cdjez/gGGAr3etNfCIiKS71+3zpaqzgdkh68J2DKvqgAhjNiYiga2BlrKawanvAcG1heqdcU10XmzpUkpt386Bo46KzvGMiaP8WgT/Brqq6iequsv9+QTnvoB/xyc8YwovqDWQOsU/HaXPtiqtoFbb6LzY0KEctXhxwfsZUwTllwhEVbeHrlTVbTGMx5ioCBwp1FJW0yblR/+yrzVQ7YJHEhCZMUVPfolgl4icGLrSXfdX7EIy5vCEjhQa5HmPFIInp1/T4OrotQaMSXL5DR+9BZglIq8AS3C+RLUBrgAujUNsxhRKaN9A4A1kACtzjuOESwu6DcaYkiPPFoGqfgm0c/cZAFzlPm7vbjOmyAkdKRTuBrJy9drFPzBjirB8J6ZR1d+B++IUizGH7fbpy4OWA+ck9t1AFrWRQoEefpgdDz4Y/eMaEwf5VR/tKSLXBSwvFJF17k+f+IRnTORmZG7kn+wc/3K/lLk0DblvYE+FerHpG/jXv/inWvXoH9eYOMivs3gYMCtguTROH0EnwG4IM0VOaN/AyFIv+4eM+jqJj+x0U2xefMECSm+xAromOeWXCNJUNbB66Jequk1VfwXKxzguYw5JrruIU6eQggb1Dewtdyy0HhCbAO66i0qZS2NzbGNiLL9EUDlwQVWvD1isFptwjCmcW6ct8z/ulzKXdiH3DQCU73J7vMMyJinklwgWumUmgojI/wGLYheSMYem/wtfkZ1zsDr5kFTnimZga2BrlVaxaw0Yk+TyGzX0b2CGiFwCfOuua4XTV3B+jOMyJiKhN489kfoMtQIqjPpGCtldxMbkLb8y1H8A/xKR04Fm7ur33XpDxhQJgR3EwzyT6eWZDwS3Bn6vcTo17S5iY/KU730EAO4Hv334myLJ10HcL2VuUHVRcFsDItQ8987YBzJmDNvvvif2r2NMDOQ7H4ExRZmvsJwzVPSloOqivsJynvPGxKemUEaGlaA2ScsSgUlKgX0DD6a+4i8qBweTwLKM4fHrIP74Y8r89lt8XsuYKLNEYJKSb7hov5S5QbOO+YaKjveex0kXxHHajBEjqLhiRcH7GVMEWSIwSSdwuOjQ1OlAcOfwSu9xHNP7sUSEZkxSskRgkkrgJaEnUp/haNnh3+YbKvqAXsn5J9VMTIDGJCFLBCap+KqL9kuZywUBQ0V9/QJ3H7ia/hdelMAIjUk+lghM0vBVFw0dJeTrF3jH25ENdftYa8CYQ2SJwCSNm99cCvgKyoXcNJZTkduyr+P1azokJDaef55t7WzCG5OcLBGYpNBu5BxyNPxE9ABPeS/kyb4ZiQkOoFEjsitWTNzrG3MYLBGYIu+eGSvY/Nd+wJl6MrQ1sNJ7HNPpkthLQu++S9msDQXvZ0wRZInAFHmvff2r/3G4qSfv817J430y4h9YoCee4Mjvf0hsDMYUkiUCU6T5ykiAU1SuWcjUk3O8rSlbr711EBtzGCwRmCLrnhkr/PcMtJTV/F9AUTnfcNFppS9IXAexMcWEJQJTJM3I3Bh0SShc38Aib2Neuvf63E82xhwSSwSmSAqcehKgg6z0P/b1DSxrMjS+QRlTTFkiMEVO6NSTr6SOol7K70H7rM05lv/rf3G8Q8vbpEls7dgx0VEYUygxTQQicraIrBKRtSJyR5jt/UVkufuzQEROjGU8pugLnXryldRRdPI4VT0D7yLe23JQIsLLW61aeMuXT3QUxhRKzBKBiHiAZ4BzgKbAxSLSNGS3n4HTVLUF8BAwPlbxmOTgu3sYnKJygUnAZ31qvfiWmI7E1KmUW78+0VEYUyixbBG0Bdaq6jpV3Q9MAXoG7qCqC1T1T3fxayA9hvGYIs539zA4ReV6hRaVU/CKUHfA8wmMMg/PPUeF1asTHYUxhRLLRFATCLzVMstdl5ergQ/CbRCRQSKyWEQWb9myJYohmqKi/wtf+e8ehvDzDGzPOYLUq/8Xn6knjSlBYpkIJMw6DbMOEemMkwhuD7ddVceramtVbV2tWrUohmiKgtB+gXDzDCjwVuPRlgSMiYHUGB47C6gVsJwObArdSURaAC8C56jqthjGY4qo0H6B0EtCAFPTehWtUULGFCOxbBF8AzQQkboikgb0A2YF7iAitYG3gctU1S6wlkAt7v8wz34Bn1VyHBff/UoCojOmZIhZIlDVbOB64CPgB+BNVV0pIoNFZLC7231AFeBZEVkqIotjFY8petqNnMOuf7z+5dtSpwIHk4DvxrHGV7+YgOgO0VtvseXUUxMdhTGFEstLQ6jqbGB2yLpxAY8HAgNjGYMpmkI7h6en3sNRstu/7OsXeLfWMM5Phn6BqlXJKVMm0VEYUyh2Z7GJu8BicuAkgZaedUDufoHzB96diBAP3YQJlP/pp0RHYUyhWCIwcXXPjBVBxeReSR0VNgl8JScmV7/AhAkcYYnAJClLBCZuQiuKDvNMDls+YhnH86/hnyciRGNKJEsEJm7+PXWp/3FLWc3gkPkFAL711iPjgW8TEJ0xJZclAhNzMzI3UueO94PuJnw89TmE4GGi23LK807rifEOz5gSL6ajhowJ7RMAp3O4Xspm/7KvNTCx3BWMOL95PMMzxmAtAhND4ZLAE6nPhO0cnpN6GjffOSreIUbP7Nn8cfrpiY7CmEKxRGBiIlwSGOaZzAVhykd8JSfS9d5ZoYdILuXKoanWwDbJyf5yTdT1f+GroPsEIHcNIZ/fOKp4jBB69lmOWLWK3Y0aJToSYw6ZtQhMVJ355LxcSWCYZ3KuJOC7c/jYgVPjHGGMvPkm5X/5JdFRGFMo1iIwUdNu5JygshHgFJILHCYK7iUhgZSOQ62stDFFgCUCc9hmZG5kaMA9Aj7DPJMZnPpe0DBRVVBfEjjzgXiGaYzJgyUCc1jCdQpD+D4Bf0vgvKeg9YD4BWmMyZclAlNoZz45jzV/7Ala1y9lLrd6plAlxVkfmgSk+UWWBIwpYiwRmEJpfPds9nkP3iscmgAgTBKodzr0fiHOkcbJvHnsePhhSic6DmMKwRKBOSShl4Jaymoe94yjnud3/7rA4aElqU/gmLvuSnQIxhSKJQITkXAdwoH9AJA7ATgrS0YSMCaZWSIwBQrXFxA6mYyPLwEokFLhaOh0l/UJGFPEWSIweQo3IqilrOZpz9PU9Dg3jQX1A7h+lWM47upJdo+AMUnCEoHJJdxloPz6AnxJYLsewRe1hyTP9JLGGMASgQkwI3MjN09dSo673FJWM8wzheYpP1FODvj3C00ACrzo7c6gEa9xfjwDNsZEhSUCAwT3A7SU1TzoeYVmnuDaOeH6AjblVObRCnfw9LDB8QrVGBNllghKsBmZG7lt2lIO5Dj3AbxYaibHyHbSJMe/T+CHPwT3BczzNmdn76k8fVLNOEVsjIkFSwQlUOAloH4pc7m1VPCNYJDHUFDXLi3Dm5UHcc2/H4p9sMaYmLNEUIKEdgK/kjqKTp4V/uX8vv0f0BR+pwpvle3DzXeO4poYx2qMiR9LBCVA6EQxwzyTudLzAWXEC+T/7X+vpvGqtyuPey/hP30zuNkuAxlT7FgiKKbC3QPQL2Uud3hep2LKPv+6cCOAvJrCNo5kTHZvpuR04dL2tfnZJpU3ptiyRFCMBHb+BorkHgCAdTlHc1v2tXyrDQHoePxRrL+mQ6zDNsYkmCWCJJfXfAD9UuZyrWcm1eVP/yUgCH8ZaJeWYVR2f6bkdAGcBPC6JQBjSgxLBEkkr2/8ofqlzGVUqZeC1uVVD+gdb0duyb4OsARgTElliaAICe3ULay+nnlA3qOAFPg+5zjuy76Sb7Uhl7avzQjrAzCmxIppIhCRs4GnAA/woqo+ErJd3O3dgL3AAFX9NtpxRPpNurgopdn+x6GjgDblVOaG7Jv8CeBtSwDGlHgxSwQi4gGeAc4EsoBvRGSWqn4fsNs5QAP3px3wnPtv1PjGzreU1QxLnULTlJ8pTTYp5JBDCjkIKah/GYjrtmi/hiKUkuCM51VhN+WY7O3MO1WuYc7NnaL5FhtjklwsWwRtgbWqug5ARKYAPYHARNATmKiqCnwtIpVEpIaq/hatIB7/aBUtZTVT0x4kldAmgTef5Xhvi9ZxnCaAiNMayAEu9g6n/4V9GHJSTYZgjDHBYpkIagIbApazyP1tP9w+NYGgRCAig4BBALVr1z6kIDbt+Juenh/wkJPrmnmxJ+Cp2ohp1w9NdCTGmCIslokg3MeuFmIfVHU8MB6gdevWubbn59hKZfl6ZxO8pCBaAjoJ5OCbKgDtrQ1gjMlfLBNBFlArYDkd2FSIfQ7LbWc1YujUv+m7/z6GeYpXH0FqilJKFMTZhuY4j1NS4YjqcPItNk2kMaZAsUwE3wANRKQusBHoB1wSss8s4Hq3/6AdsDOa/QMA57u1cW6bBv2y74vmoWOqdGoKj/Zu4Y/fGGNiJWaJQFWzReR64COc4aMvq+pKERnsbh8HzMYZOroWZ/jolbGI5fyTatoHqjHG5CGm9xGo6mycD/vAdeMCHitwXSxjMMYYk7+URAdgjDEmsSwRGGNMCWeJwBhjSjhLBMYYU8KJhlYlK+JEZAvwSyGfXhXYGsVwkoGdc8lg51wyHM45H6eq1cJtSLpEcDhEZLGqtk50HPFk51wy2DmXDLE6Z7s0ZIwxJZwlAmOMKeFKWiIYn+gAEsDOuWSwcy4ZYnLOJaqPwBhjTG4lrUVgjDEmhCUCY4wp4YplIhCRs0VklYisFZE7wmwXEXna3b5cRFomIs5oiuCc+7vnulxEFojIiYmIM5oKOueA/dqIiFdELoxnfLEQyTmLSCcRWSoiK0Xks3jHGG0R/G1XFJF3RWSZe84xqWIcLyLysoj8ISLf5bE9+p9fqlqsfnBKXv8E1APSgGVA05B9ugEf4Ezi1R5YmOi443DO/wIqu4/PKQnnHLDfJzhVcC9MdNxx+D1XwpkXvLa7XD3RccfhnO8CHnUfVwO2A2mJjv0wzvlUoCXwXR7bo/75VRxbBG2Btaq6TlX3A1OAniH79AQmquNroJKI1Ih3oFFU4Dmr6gJV/dNd/BpnNrhkFsnvGeAGYDrwRzyDi5FIzvkS4G1V/RVAVZP9vCM5ZwUqiIgAR+Akguz4hhk9qvo5zjnkJeqfX8UxEdQENgQsZ7nrDnWfZHKo53M1zjeKZFbgOYtITeACYBzFQyS/54ZAZRGZJyJLROTyuEUXG5Gc81igCc40tyuAm1SL9QTlUf/8iunENAkiYdaFjpGNZJ9kEvH5iEhnnERwckwjir1IznkMcLuqep0vi0kvknNOBVoBXYCywFci8rWqro51cDESyTmfBSwFTgeOB+aIyBequivGsSVK1D+/imMiyAJqBSyn43xTONR9kklE5yMiLYAXgXNUdVucYouVSM65NTDFTQJVgW4ikq2qM+ISYfRF+re9VVX3AHtE5HPgRCBZE0Ek53wl8Ig6F9DXisjPQGNgUXxCjLuof34Vx0tD3wANRKSuiKQB/YBZIfvMAi53e9/bAztV9bd4BxpFBZ6ziNQG3gYuS+Jvh4EKPGdVrauqdVS1DvAWMCSJkwBE9rc9EzhFRFJFpBzQDvghznFGUyTn/CtOCwgRORpoBKyLa5TxFfXPr2LXIlDVbBG5HvgIZ8TBy6q6UkQGu9vH4Ywg6QasBfbifKNIWhGe831AFeBZ9xtytiZx5cYIz7lYieScVfUHEfkQWA7kAC+qathhiMkgwt/zQ8AEEVmBc9nkdlVN2vLUIvIG0AmoKiJZwP1AKYjd55eVmDDGmBKuOF4aMsYYcwgsERhjTAlnicAYY0o4SwTGGFPCWSIwxpgSzhJBEhKRSiIyJMavcbaILBKRH91KllPdexF821NFZKuIjDqM12gtIk/nsW29iFQt7LEjeO27onSc0u57s1ZEFopInQL2nxVYVVJEjhORuW4VyXkikh6w7TG3muYPbrVJcdd/4f5OlorIJhGZcRjxz3Mrey51X2dQwLbZIlKpsMc+hBhGisgGEdkdsj7P91ZErhCRNe7PFQHr67r7rnGfmxbr+IuFRFfas59CVSesQ96VCT1ROP4JwBqgScC6HsCpAcvdgPk4lSElBue4Hqgaw/dwd5SOMwQY5z7uB0zNZ99ewOTA3x0wDbjCfXw6MMl9/C/3/fW4P18BncIcczpw+WHEPw9o7T4+CviTOFfuxKmgWSP0d5LXe+vGuc79t7L72FdZ902gn/t4HHBtPM8lWX8SHoD9FOKX5lRg/BunvsrjODeffOp+yHwfmiiAW4Hh7uPjgQ+BJcAXQOMwx58EXFlADJOAi9zX7ZDHPitwyiILsM33geU+9ww37vfcdVWA/wGZwPPAL75EAFyKUy5gqbvNE/I65wBvBix3At51H1/sxvEdB0sVPwJ43eO9Hslr5PM+fOQ7f5wbNLcSJjHiVMX8Emga8rtZCaS7jwXY5T7u4P6OygLlgMUEJGZ3nwo4H9xHFhBjJ5wP/LeAH4HXOXgP0TwOJoLaOOULPO7yepzSHHVw7k5+wY33f0BZd58bcf7mlgNTDvPvOjQRhH1v3d/p8wH7Pe+uE3ef1ID38KNE/39Nhh+7NJSc7gB+UtUMVb3NXdcWuFtVmxbw3PHADaraCidBPBtmn2bAt3kdQETK4tzS/x7wBs5/wnDmAx3d460DTnHXt8cphR3ofuBLVT0J5xb62u5rNQH6Ah1VNQPnA7x/yHPnAO1FpLy73BeYKiLHAo/ifNPOANqIyPmqegfwt/v+9c/vNdzLC0vD/PiqevorQapqNrATJ6mFegh4AudO0EDLgN7u4wtwyilXUdWvcJLsb+7PR6oaWiriAmCuRlZc7SRgKE4iqofze/F5XUSWA6uAh1TVG+b5DYBnVLUZsCMg5juAk1S1BTA49Eki0iiP929pBJed8npv86q+WQXY4e4buN4UoNiVmCjBFqnqz/ntICJH4FxymCYHq3GWLuA5VYC5ON9Kx6vqaOA84FNV3Ssi04F7ReTfYT5AvsCZZOMX4DlgkDilober6m4Jrgh6Ks6lE1T1fRHxzZ3QBaea5jfu/mUJmVtAnTIEHwLdReQt4FxgGE4CmKeqW9xzed19nRkhceb5GqraN7/3hwgqQYpIBlBfVf8dpg/hVmCsiAwAPgc2AtkiUh+ntLKvz2COiJyqTq16n4txighGYpGqZrnxLMX5lv+lu62/qi4WkWrAAhH5UFV/CXn+z6q61H28xH0+OC2B191+ihmhL6qqq3CScGHk9d4e6npTAEsExceegMfZBA8EKOP+m4LzjSmjgGOtxJkhaZk6VUozRORWnMsb4HwAdRSR9e5yFaCziDQCrnHXdcP5YLsO59v93TjfYC/ESRDhhPtPK8CrqnpnATFPdV9rO/CNqv4lEnHt6TxfQ0Sm4hQxC/Wkqk7kYCXILBFJBSqSe1KRDkAr9/1KBaqLyDxV7aSqm3AToJuoe6vqTrfT9mtV3e1u+wCnJfW5u1wFpxV4QYTn+E/AYy9h/u+r6hYR+RanUF1oIgh9fln38bk4ybUHzheCZgHfyHH/JqbmEVMnVd2RT8x5vbdZOJe7fNJxLnFtxZmkJdWNIdmrCseNXRpKTn/hXB/Oy2acD5sqIlIa5xs87iWEn0WkD/jnPg03d/FjwN3uJROfcu5zjsSZy6C2HqzseR1wsao+415uyVDVTaq6AecacwNVXYfzDfRWwieCzzl4OeYcnE5AcFojF4pIdXfbUSJyXJjnz8NJXtdw8INnIXCaiFQVEQ9OAvPN4XtAREoV9Bqq2jfgnAJ/JrrPnQX4Rq1cCHyiqkEJTVWfU9Vj3ffqZGC1qnZyX6uqiPj+H94JvOw+/tWNPdWN8zSCq4j2welf2edbISJtRWQihSROtdKTcAYARLJ/ClBLVT/FaYFV4uCXBcBpEeTx/mUUkAQg7/f2I6CriFQWkcpAV5xLZ4pzOc03N/UVONVYTQEsESQh91v6fBH5TkQeD7P9APAgzgfhezgdhD79gatFZBnON/9c0zuq6grgJmCiOMNH5+NcppiM8+31E1UN/IY4E+jhJp1QCzlYC/8LnGu2X4bZ7wHgVPcbaVecD0JU9XvgHuB/7nXsOTgjTEJj9rrneo77L+qU5r0T58NhGfCtqvo+GMYDy0Xk9UhfIw8vAVVEZC1wM841c8B/CaYgnYBVIrIaOBoY6a5/C+cDeYUb+zJVfTfgef1w+mcC1cYZRHCoXndjXQJMUNUlET7PA7wmTtXPTOA/EXy45yLOMNksoJyIZInIcHdT2PdWVbfj9Ll84/486K4DuB242X1OFfcYpgBWfdSYYsL9UjBJVZcnOhaTXCwRGGNMCWeXhowxpoSzRGCMMSWcJQJjjCnhLBEYY0wJZ4nAGGNKOEsExhhTwv0/RqCh9zVP3e4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 2.109 2.208\n",
      "fractional expected GOP seats =  8.446  out of  14 seats for GA\n",
      "simpler expected GOP seats =  8.8019  out of  14 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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